🤖Prompt Engineering: Vectors (Just the Quotes)

"Different algorithms may perform better on different types of text data and will have different vector sizes. The choice of algorithm can have a significant impact on the quality of the resulting embeddings. Additionally, open-source alternatives may require more customization and finetuning than closed-source products, but they also provide greater flexibility and control over the embedding process." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"Data drift manifests in several distinct ways. Input drift typically shows up as an increase in adversarial or malformed queries that deviate from the original training or design expectations. This can stress the system’s robustness and degrade output quality. Retriever drift occurs when the relevance of the documents returned by retrieval components declines, even if the retrieval algorithms and configurations remain unchanged. Similarly, embedding drift arises when the vector representations used to compare semantic similarity become less effective, causing retrieval systems to fail despite stable system parameters." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"LLM-centric workloads change everything. Now the raw material is heterogeneous text, code, images, audio, and chat logs whose value depends on semantic richness - that is, the informational value of the content - rather than a rigid structure. Pipelines must tokenize, chunk, embed, and version this content; store it in vector indexes for similarity search; and apply filters for personally identifiable information, toxicity, and licensing constraints. Instead of ETL jobs, teams run continuous ingestion and reembedding loops so that RAG systems stay fresh, and they log every prompt–response pair so that the inputs and outputs can be evaluated and improve the future performance of this system. Data quality in this context is judged by grounding, factuality, and bias metrics - attributes that require automated red-teaming and humanin-the-loop (HITL) review rather than the data structure violation checks of the past." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Vector databases are designed to store and index highdimensional embeddings - dense numeric vectors that capture the semantic meaning of text, images, audio, or other content. Instead of looking for exact matches, they use approximate nearest neighbor (ANN) algorithms to return the items whose vectors lie closest to a query vector in that multidimensional space. This makes them the engine behind semantic search, recommendation systems, image-or-audio similarity matching, and retrieval augmented generation (RAG) pipelines that supply LLM prompts with relevant context in milliseconds." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Although GenAI gets most of the attention for its ability to create new content, successful AI applications often combine multiple approaches. Generative models aren’t always the optimal choice for every component of an AI system. Traditional ML models, rule-based systems, and algorithmic approaches can offer significant advantages in speed, accuracy, cost-effectiveness, and reliability when used strategically alongside GenAI." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Embeddings are mathematical representations of text, images, or other input types. They capture semantic meaning by mapping words, phrases, or documents to vectors in a high-dimensional space. LLMs have become exceptionally proficient at creating semantically representative embeddings because this capability forms the foundation for LLM generation. [...] While the latest LLMs provide massive context windows, encoding entire documents in a single embedding vector is often not advisable. You’ll lose the nuances of the different parts of a text, lose critical information, and hinder the RAG system from retrieving relevant information. Plus, it gets very expensive to use large context windows at scale. It’s preferable to chunk documents into information-dense and self-consistent chunks of text." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"RAG applications must be built with semantics, metadata, and governance in mind. The retrieved information must be high-quality, secure, and appropriate for the user’s role. Equally important is monitoring and management: checking whether source data has changed, ensuring vector stores remain accurate, and watching for hallucinations or data leakage. Organizations are definitely starting to experiment with RAG models today; some are putting them into production applications. Some believe that using RAG helps mitigate hallucinations because it is grounded in trusted organizational data." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"RAG is a technique that combines the ability to make use of specialized domain knowledge with the techniques of prompt engineering. In a RAG application, documents that the application should have access to are split into chunks, such as overlapping blocks of text, and embedded as high-dimensional vectors. [...] A RAG application then stores the embeddings created from the documents along with metadata about each embedding, including a link to its source document and position in the document, in a vector database. When a user submits a prompt to an application with RAG capabilities, the application encodes the user’s query using the same embedding model used to encode the documents and finds the closest matches (aka nearest neighbors) between the user prompt and the chunks in the vector database. Finally, a new prompt is created that combines the retrieved snippets of text with a templated instruction. This new prompt is sent to the LLM for inference." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Sentence embeddings are dense vector representations of entire sentences or phrases, capturing their semantic meaning in a fixed-dimensional space. Unlike word embeddings, which represent individual words, sentence embeddings are designed to encode the meaning of longer text sequences. Two prominent techniques for generating sentence embeddings are SBERT(sentence-BERT) and dense retriever. [...] SBERT is a modification of the BERT architecture specifically designed for generating sentence embeddings. Traditional BERT outputs contextualized word embeddings, but SBERT fine-tunes BERT to produce fixed-size sentence embeddings by applying a pooling operation (e.g., mean pooling, max pooling, or CLS token pooling) over the token embeddings." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"The foundational form of RAG, often called naive RAG, follows a straightforward pattern. A pipeline retrieves supporting context from external sources such as enterprise documents, knowledge bases, or structured datasets and appends that information to the model’s prompt before inference. In the most common implementation, each document is converted into an embedding, a numerical representation of its semantic meaning, using either the same foundation model or a specialized embedding model. When a user submits a query, the system performs a vector similarity search to find documents whose embeddings most closely match the query’s vector representation, and the retrieved content is concatenated with the user query before being passed to the language model." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

🪙Business Intelligence: Data Security (Just the Quotes)

"You cannot boil the ocean; you have to prioritize your data integration deliverables. An enterprise-wide data integration effort must be carved up into small iterative projects, starting with the most critical data and working down to the less significant data. The business people working with the data integration team must determine which data is most appropriate for integration. Some data might not be suitable for integration at all, such as department-specific data, highly secured data, and data that is too risky to integrate. The team also needs to look at historical data and decide how much of it to include in the data integration process." (Sid Adelman et al, "Data Strategy" 1st Ed., 2005)

"Data governance policies must not enforce constraints on data - Data governance intends to control the level of democracy within the data lake. Its sole purpose of existence is to maintain the quality level through audits, compliance, and timely checks. Data flow, either by its size or quality, must not be constrained through governance norms. [...] Effective data governance elevates confidence in data lake quality and stability, which is a critical factor to data lake success story. Data compliance, data sharing, risk and privacy evaluation, access management, and data security are all factors that impact regulation." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"A data product encapsulates more than just the data. It needs to contain all the structural components needed to manifest its baseline usability characteristics - discoverable, understandable, addressable, etc. - in an autonomous fashion, while continuing to share data in a compliant and secure manner." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data privacy, data confidentiality, and data protection are sometimes incorrectly diluted with security. For example, data privacy is related to, but not the same as, data security. Data security is concerned with assuring the confidentiality, integrity, and availability of data. Data privacy focuses on how and to what extent businesses may collect and process information about individuals." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"In the case of data mesh, a data product is an architectural quantum. It is the smallest unit of architecture that can be independently deployed and managed. It has high functional cohesion, i.e., performing a specific analytical transformation and securely sharing the result as domain-oriented analytical data. It has all the structural components that it requires to do its function: the transformation code, the data, the metadata, the policies that govern the data, and its dependencies to infrastructure." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"A data fabric is a pattern that is very similar to a data mesh in that both provide solutions encompassing data governance and self-service: discovery, access, security, integration, transformation, and lineage. [...] In simple terms, a data fabric is a metadriven means of connecting disparate sets of data and related tools to provide a cohesive data experience and to deliver data in a self-service manner." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"Any project execution would be very difficult without implementation and usage of the right product capabilities. The selected products should support the data sources and platforms in your organization and provide AI-augmented functionality to ingest and automatically enrich metadata, allowing business users to easily understand, collaborate, enrich, and access the right data, to quickly establish an environment for highly automated and consistent governance and automatically secure data across the organization."(Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Creating data products requires that domain owners have a degree of autonomy in modeling, building, and delivering data to their consumers. However, by empowering them with autonomy and independence, you run the risk of a significant technological sprawl across data product implementations, making it more difficult for consumers to use the data products for their own ends. Federated governance focuses on finding an equilibrium between the needs of the consumers, the autonomy of the data product owners, the business compliance and security requirements, and global data product requirements." (Adam Bellemare, "Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)

"Data lineage is the path the data took from its source origin, the stops it made along the way, and its destination. This includes information on all the systems it passed through, how it was cleansed, what it was enriched with, and how it was secured. Capturing all that metadata is difficult because many of those systems and applications don’t share information. It’s up to you to assemble the data’s path by pulling metadata from all those systems/applications and assembling them in hopes that you find the path your data took from its current location (destination) to its source system. Lineage is probably the hardest piece of metadata to acquire for either streaming or batching data pipelines." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"Manage data as a strategic asset that evolves into a data product. The premise here is to stop managing data as a byproduct and create an ecosystem that manages data as a valuable strategic asset that can evolve into a data product. Data producers are accountable for managing the life cycle of data from creation to end of life and ensuring it creates business value along the way for data consumers. This requires data that is governed, trusted, protected, secure, and easily accessible. Move data from technical data assets to Data Products by operationalizing data for high scale sharing." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

"The term data governance is used for the processes and responsibilities that define, manage, and enforce access, privacy, availability, and security of the organization’s data. It typically includes a set of policies, rules, and data classifications and functionality to monitor and enforce compliance. As stated earlier, we use the term AI governance in a broader sense, also including AI artefacts." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"A data mesh is a decentralized data architecture with four specific characteristics. First, it requires independent teams within designated domains to own their analytical data. Second, in a data mesh, data is treated and served as a product to help the data consumer to discover, trust, and utilize it for whatever purpose they like. Third, it relies on automated infrastructure provisioning. And fourth, it uses governance to ensure that all the independent data products are secure and follow global rules." (James Serra, "Deciphering Data Architectures", 2024)

"A data mesh splits the boundaries of the exchange of data into multiple data products. This provides a unique opportunity to partially distribute the responsibility of data security. Each data product team can be made responsible for how their data should be accessed and what privacy policies should be applied." (Aniruddha Deswandikar,"Engineering Data Mesh in Azure Cloud", 2024)

"Delta Lake is a transactional storage software layer that runs on top of an existing data lake and adds RDW-like features that improve the lake’s reliability, security, and performance. Delta Lake itself is not storage. In most cases, it’s easy to turn a data lake into a Delta Lake; all you need to do is specify, when you are storing data to your data lake, that you want to save it in Delta Lake format (as opposed to other formats, like CSV or JSON)." (James Serra, "Deciphering Data Architectures", 2024)

"Data security is about preventing unauthorized access to data and the policies and methods surrounding this access. It also protects the system from hackers and malicious users who could steal data. Data privacy, on the other hand, is about collecting, retaining, and recycling personal and sensitive data. There could be a few overlaps between security and privacy." (Aniruddha Deswandikar, "Engineering Data Mesh in Azure Cloud", 2024)

"Federation is about providing autonomy to each data product owner to make their own decisions about the storage, computing, and sharing of data. However, this autonomy cannot come at a risk to the security and compliance standards of the company." (Aniruddha Deswandikar, "Engineering Data Mesh in Azure Cloud", 2024)

"Governance refers to an organization’s framework to exercise direction and control over a specific domain. In the context of data, it could include rules, protocols, and systems to manage data quality, security, and accessibility."  (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

🖍️Chris Minnick - Collected Quotes

"[...] a transformer is a neural network that uses attention to understand and generate sequences of data efficiently and flexibly, allowing it to capture complex relationships across long inputs. Unlike older models, transformers process all parts of the input in parallel, making them much faster and more scalable than older types of neural networks." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Although GenAI gets most of the attention for its ability to create new content, successful AI applications often combine multiple approaches. Generative models aren’t always the optimal choice for every component of an AI system. Traditional ML models, rule-based systems, and algorithmic approaches can offer significant advantages in speed, accuracy, cost-effectiveness, and reliability when used strategically alongside GenAI." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Because LLMs are probabilistic, it’s possible for them to generate responses that are biased, toxic, false, or off-topic for your application. If a user submits prompts to an LLM that contain profanity or hate speech, it increases the chances that the generated content will contain inappropriate language. Such interactions can create legal liability or brand damage. LLMs are also vulnerable to attack by malicious users who attempt to convince the LLM to reveal sensitive corporate data or personally identifiable information (PII)." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Deep learning, a subset of ML, employs layers of artificial neural networks to identify patterns in data. A key attribute of deep learning is its ability to enable models to automatically learn hierarchical features from raw data, rather than relying on manually engineered features from structured data. Prior to deep learning, defining relevant features such as edges, color histograms, or keywords was typically necessary to train a model to recognize objects or sentiment. In contrast, deep learning models autonomously learn these features during training by optimizing the parameters of each layer to minimize error on the task at hand." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Hybrid integration combines a client-side model or significant frontend data processing with backend data processing. In practice, hybrid integration often blends the use of two or more different types of AI models. For example, blending the creativity of a generative model with the potential accuracy of reinforcement learning can lead to more trustworthy results than you might get from a generative model by itself. Other combinations of different types of models could include integrating neural networks with knowledge graphs or integrating symbolic AI with machine learning."(Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"In backend service integration, the application accesses an AI model or API through a server-side service. Using server-side resources to access the model improves the application’s security, simplifies monitoring and logging, and improves the scalability of the application. The trade-off is that it increases the complexity of the backend, and network round trips introduce latency into the application. Backend service integration is the right approach for applications that require access to a powerful AI model and that don’t require much client-side logic, such as search applications, chatbots, and data analysis." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Mixture of experts (MoE) architectures are neural networks that contain many different 'expert' submodels. Instead of using them all at once, the model chooses a few experts to handle each input. [...] A gating network in an MoE architecture handles routine tasks and decides which experts are activated for each prompt. Most MoE language models and their expert submodels are transformer-based. The most important benefit of MoE is that it can be made more capable by adding additional experts, rather than increasing the size of a single model. Because only a subset of experts is activated at a time, unnecessary computation is reduced." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Prompt engineering is the practice of crafting, optimizing, and managing prompts to guide large language models (LLMs) toward producing useful, specific, and high-quality outputs. It ranges from simple one-off instructions to structured templates and programmatic prompt management in production systems. [...] Prompt engineering is all about providing context to better align your question with the type of output you want the LLM to generate." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"RAG is a technique that combines the ability to make use of specialized domain knowledge with the techniques of prompt engineering. In a RAG application, documents that the application should have access to are split into chunks, such as overlapping blocks of text, and embedded as high-dimensional vectors. [...] A RAG application then stores the embeddings created from the documents along with metadata about each embedding, including a link to its source document and position in the document, in a vector database. When a user submits a prompt to an application with RAG capabilities, the application encodes the user’s query using the same embedding model used to encode the documents and finds the closest matches (aka nearest neighbors) between the user prompt and the chunks in the vector database. Finally, a new prompt is created that combines the retrieved snippets of text with a templated instruction. This new prompt is sent to the LLM for inference." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Self-attention is a mechanism where, for each token, the model looks at all other tokens in the input and computes weighted importance scores (attention weights) to decide how much each other token should contribute to its representation. Prior to self-attention, each token would be evaluated sequentially. This capability makes transformers especially good at handling long-range dependencies in text and is one of the reasons they have become the foundation for nearly all modern LLMs." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Technical features are the design and training decisions that are made that determine aspects such as a model’s use-case suitability and performance metrics. By understanding the basics of these technical features, you can better evaluate, assess, and predict how models will perform. These technical features include architecture, parameter count, training data, fine-tuning and specialized tuning, context window, and unique functionalities." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026) 

🤖Prompt Engineering: Reasoning (Just the Quotes)

"LLMs are trained on large volumes of data, which inherently provides them with an immense knowledge base and understanding of different languages. Yet, LLMs at their core are complex text completion engines. Since this knowledge and understanding of language is compressed in a very high-dimensional latent space. LLMs end up using these in a very fluid and intelligible way (which often leads to hallucinations). In order to guide LLMs to focus on specific topics or pieces of information to solve certain tasks, (for instance, question-answering from a given piece of text), it is important to provide contextual information explicitly. While most current generations of LLMs have extremely wide context windows, it is recommended to preprocess context into overlapping smaller chunks for better results, reduced latency, and so on. For similar reasons, it is also recommended to preprocess contextual information in clear and task-specific formats. This aspect of context preprocessing is extremely useful in Retrieval-Gugmented Generation (RAG) scenarios." (Joseph Babcock & Raghav Bali, "Generative AI with Python and PyTorch" 2nd. Ed., 2025)

"Semantic Kernel is a framework designed to simplify integrating LLMs into applications that require dynamic knowledge, reasoning, and state tracking. It’s particularly useful when you want to build complex, modular AI systems that can interact with external APIs, knowledge bases, or decision-making processes. Semantic Kernel focuses on building more flexible AI systems that can handle a variety of tasks beyond just generating text. It allows for modularity, enabling developers to easily combine different components - such as embeddings, prompt templates, and custom functions - in a cohesive manner." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Agentic AI extends GenAI by embedding intelligence within autonomous or semi-autonomous systems that can plan, reason, and take actions within defined boundaries. Instead of simply generating a report, an agentic system might determine which data it needs, retrieve that data from multiple sources, perform analysis, summarize the results, and then trigger follow-up workflows, all while maintaining auditability and alignment with governance policies." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Human-in-the-loop (HITL) is a paradigm that formally integrates human expertise into the AI workflow, creating a collaborative partnership between human and machine intelligence. This approach is essential for high-consequence applications where full automation is too risky, such as medical diagnosis, legal contract review, or content moderation. In a RAG system, the human acts as a validator, auditor, and final decision-maker. The AI handles the heavy lifting of information retrieval and draft generation, while the human provides the critical judgment, context, and ethical reasoning that the AI lacks." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026) 

"[...] KGs and LLMs can be the foundation for different types of reasoning, complementing each other in intelligent systems. We can use KGs for tasks that require precise, rule-based reasoning and explicit knowledge representation, and LLMs for tasks involving pattern recognition, context understanding, handling ambiguity or incomplete information, and reasoning about graph structures and their derived metrics. However, neither approach inherently possesses common-sense reasoning capabilities comparable to those of humans, and they often fail to make intuitive leaps or understand the implicit context that would be obvious to a person. These limitations underscore the importance of carefully considering the strengths and weaknesses of each approach when designing intelligent systems and potentially developing a powerful hybrid IAS." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)

"Knowledge representation is the 'language' that AI systems use to structure, encode, and communicate information about the domain they are modelling. AI systems need it to reason or predict, and we use it to interpret the results they produce. The choice of how to represent knowledge can significantly affect the capabilities and limitations of the AI system." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)

"LangGraph handles the perception, reasoning, and action flow while maintaining memory and context across tasks. In this sense, it functions as both the development environment and the orchestration layer - coordinating the steps of perception, reasoning, and action while managing connections to external systems. Underneath this, emerging standards like MCP ensure that agents can connect securely and consistently to tools and data sources, making agentic architectures portable and scalable across platforms. Taken together, the orchestration layer and emerging interoperability standards like MCP form the foundation for scalable agentic AI. They make it possible for agents to perceive, reason, act, and learn in coordinated ways across complex environments, translating autonomous intelligence into practical, enterprise-grade capability." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"[...] LLMs raise serious concerns about ethics, bias and fairness, errors in reasoning, hallucinations, and misuse (e.g., misinformation and disinformation). These concerns are exacerbated by modern LLMs being both literal and figurative 'black boxes': Literal black boxes because many advanced AI systems are proprietary and the weights (trained parameters of the models) are not released to the public; and figurative black boxes because even the open-source AI models are so complicated that understanding them and developing safety guardrails has thus far proven extremely difficult." (Mike X Cohen,"50 ML Projects To Understand LLMs", 2026)

"The Chain of Thought pattern uses chain-of-thought (CoT) reasoning, which is a prompting technique that enhances LLMs’ problem-solving capabilities by encouraging them to break down complex problems into intermediate reasoning steps before arriving at a final answer. [...] CoT involves modifying the prompting strategy - instead of asking for a direct answer, the prompt explicitly requests or demonstrates a step-by-step reasoning process. There are three variants: Zero-shot CoT, Few-shot CoT, and Auto-CoT. [...] In Zero-shot CoT, you request a step-by-step reasoning by simply adding the phrase think step-by-step to the prompt. [...] In cases where the model’s pretrained capabilities do not include the specific logic needed, you can try Few-shot CoT, which involves providing the model with examples of step-by-step reasoning for similar problems before asking it to solve a new problem. In Few-shot CoT, you demonstrate the desired reasoning pattern through examples. [...] In the Auto-CoT variant, you add demonstrative examples to a wide variety of questions to a database called an example store. Most people use a document database (in which the examples are indexed by keywords in the question text) or a vector database (in which the examples are indexed by the embedding of the questions) for the example store." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"The Reflection pattern is an agentic approach in which an AI system evaluates its own reasoning, decisions, and outputs before finalizing them. This self-monitoring process allows the system to identify errors, improve solutions, and refine its approach to problems. Here, the AI system as a whole is reflecting and improving, and the foundational model used to generate the response remains the same. You’ll sometimes see this process referred to as self-reflection, but there’s no reason why the evaluation has to be done by an LLM at all, let alone by the same LLM that generates the content." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

🪙Business Intelligence: Architecture (Just the Quotes)

"Having a purposeless or poorly performing dashboard is more common than not. This happens when the underlying architecture is not designed properly to support the needs of dashboard interaction. There is an obvious disconnect between the design of the data warehouse and the design of the dashboards. The people who design the data warehouse do not know what the dashboard will do; and the people who design the dashboards do not know how the data warehouse was designed, resulting in a lack of cohesion between the two. A similar disconnect can also exist between the dashboard designer and the business analyst, resulting in a dashboard that may look beautiful and dazzling but brings very little business value." (Nils H Rasmussen et al, "Business Dashboards: A visual catalog for design and deployment", 2009)

"A defining characteristic of the data lakehouse architecture is allowing direct access to data as files while retaining the valuable properties of a data warehouse. Just do both!" (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"Data mesh [...] reduces points of centralization that act as coordination bottlenecks. It finds a new way of decomposing the data architecture without slowing the organization down with synchronizations. It removes the gap between where the data originates and where it gets used and removes the accidental complexities - aka pipelines - that happen in between the two planes of data. Data mesh departs from data myths such as a single source of truth, or one tightly controlled canonical data model." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"The data lakehouse architecture presents an opportunity comparable to the one seen during the early years of the data warehouse market. The unique ability of the lakehouse to manage data in an open environment, blend all varieties of data from all parts of the enterprise, and combine the data science focus of the data lake with the end user analytics of the data warehouse will unlock incredible value for organizations. [...] The lakehouse architecture equally makes it natural to manage and apply models where the data lives." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"The data warehouse approach is also referred to as data marts with the usual distinction that a data mart serves a single department in an organization, while a data warehouse serves the larger organization integrating across multiple departments. Regardless of their scope, from the architectural modeling perspective they both have similar characteristics." (Zhamak Dehghani,"Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data mesh relies on a distributed architecture that consists of domains. Each domain is an independent unit of data and its associated storage and compute components. When an organization contains various product units, each with its own data needs, each product team owns a domain that is operated and governed independently by the product team. […] Data mesh has a unique value proposition, not just offering scale of infrastructure and scenarios but also helping shift the organization’s culture around data," (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)

"Gaining more insight into data, simplifying data access, enabling shopping-for-data, augmenting traditional data governance, generating active metadata, and accelerating development of products and services are enabled by infusing AI into the Data Fabric architecture. An AI-infused Data Fabric is not only leveraging AI but also likewise an architecture to manage and deal with AI artefacts, including AI models, pipelines, etc." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"The promise of a cloud data lake architecture lies in the boundless diversity of scenarios that it enables." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)

"Understanding modern data architectures and sound data engineering principles and practices are crucial to ensure that your AI and BI strategies are reliable and defensible. Generated insights are going to be as good as the quality of the underlying data, so the upfront effort put into understanding the data, modeling it, and transforming it per the business needs goes a long way to foster innovation, productivity, and agility in your data teams." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)

"Where Data Mesh differs from Data Fabric is that it has fixed requirements for the Self-Service platform focused on organizing and managing Data Products by business domain. Another difference is Data Fabric supports managing data as an asset and as a product. A Data Product can be composed of assets that have been governed and managed in a Data Fabric architecture. Data Fabric does not have these fixed requirements, although it inherently supports isolating data and Data Governance enforcement via metadata by business domain. You can think of a Data Mesh Self-Service data platform as supporting separate, independent companies (business domains), although the key criteria are that it does not create data silos and attains data sharing across these companies in a secure, quick, and easy manner. In Data Mesh, Data Products are created and managed by federated business domains and a data platform requires capabilities that enable data and policy federation. This is where a Data Fabric solution can also address Data Mesh’s requirements." (Sonia Mezzetta, "Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently", 2023)

"It is very important to understand that data mesh is a concept, not a technology. It is all about an organizational and cultural shift within companies. The technology used to build a data mesh could follow the modern data warehouse, data fabric, or data lakehouse architecture - or domains could even follow different architectures." (James Serra, "Deciphering Data Architectures", 2024)

"To explain a data mesh in one sentence, a data mesh is a centrally managed network of decentralized data products. The data mesh breaks the central data lake into decentralized islands of data that are owned by the teams that generate the data. The data mesh architecture proposes that data be treated like a product, with each team producing its own data/output using its own choice of tools arranged in an architecture that works for them. This team completely owns the data/output they produce and exposes it for others to consume in a way they deem fit for their data." (Aniruddha Deswandikar,"Engineering Data Mesh in Azure Cloud", 2024)

"Establishing a comprehensive observability architecture necessitates a systematic approach that spans the entirety of the data pipeline, from initial telemetry collection to actionable insights accessible by diverse stakeholders. The core objective is to unify distributed data sources - metrics, logs, traces, and quality signals - into a coherent framework that enables rapid diagnosis, continuous monitoring, and strategic decision-making." (William Smith, "Soda Core for Modern Data Quality and Observability: The Complete Guide for Developers and Engineers", 2025)

"Modern complex organizations increasingly confront the challenge of ensuring data quality at scale without centralizing validation activities into a single bottlenecked team. The data mesh paradigm and federated controls emerge as pivotal architectural styles and organizational patterns that enable decentralized, self-serve data quality validation while preserving coherence and reliability across diverse data products." (William Smith, "Great Expectations for Modern Data Quality: The Complete Guide for Developers and Engineers", 2025)

"A solid data foundation is critical for AI success. The foundation can grow in phases, but it needs to be there. AI will add pressure to the data foundation. Modern workloads require data that is connected, interpretable, high quality, and governed. Modern workloads utilize diverse data, and this is especially true with the popularity of GenAI. Trustworthy AI depends on knowing where the data came from and how it has changed. Modern architectures including the lakehouse and the data fabric exist because the traditional ways of managing data no longer fit the scale or complexity of what organizations need to do today. In other words, there is no path toward enterprise AI without a strong data foundation. This was echoed in the expert advices. Experts spoke about the dangers of 'vanity projects', particularly with GenAI, when foundational data issues were ignored. Others described how their companies moved faster specifically because they had already invested in governance, semantic layers, lineage, and observability." (Fern Halper, "Data Makes the World Go 'Round", 2026)

🪙Business Intelligence: Ownership (Just the Quotes)

"[...] the governance function is accountable to define what constitutes data quality and how each data product communicates that in a standard way. It’s no longer accountable for the quality of each data product. The platform team is accountable to build capabilities to validate the quality of the data and communicate its quality metrics, and each domain (data product owner) is accountable to adhere to the quality standards and provide quality data products." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"[...] data mesh introduces a fundamental shift that the owners of the data products must communicate and guarantee an acceptable level of quality and trustworthiness - specific to their domain - as an intrinsic characteristic of their data product. This means cleansing and running automated data integrity tests at the point of the creation of a data product." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"A core premise of data mesh is federating data ownership among domain data owners who are responsible for their data as a product. Offering the data as a product requires the data to be discoverable and to have explicitly stated quality characteristics and a clearly defined access method. Such requirements are at the core of what data catalogs support. With support for data labeling, curation, and crowdsourced feedback, data catalogs are well positioned to offer data as a product. Furthermore, data catalogs support the enforcement of compliant data usage, which becomes more important when data ownership is not managed centrally." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)

"A Data Mesh views data primarily as organized around domain owners who create business-focused data products, which can be aggregated and consumed across distributed consumers, organizations, and Line of Business (LoBs) in a self-service and shopping-for-data fashion. Transforming data from disparate data sources to be consumed as data-as-a-product is an essential paradigm of any Data Mesh." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"A domain has two main roles: data product engineer (or just data engineer) and the data product owner (or data product manager, or data steward). These roles can be the same or dedicated people in the domain. Data product owners must have a deep understanding of who their data consumers are, how the data is used, and what methods are used to consume the data. This will help ensure that the data products meet the needs of their use cases. Data product engineers are responsible for creating data products that are high quality, reliable, and usable by consumers. It should be possible to extend existing domain roles to include these domain roles with minimal effort." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)

"Building a data product is enabled by the data domain owner; however, building a data product itself is primarily driven by the data product owner, which can be a marketing or a customer care organization, an after-sales team, or even an individual business user. The data product owner is collaborating with data engineers, data scientists, and other subject matter experts throughout the entire data product build process." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Data mesh architectures are inherently decentralized, and significant responsibility is delegated to the data product owners. A data mesh also benefits from a degree of centralization in the form of data product compatibility and common self-service tooling. Differing opinions, preferences, business requirements, legal constraints, technologies, and technical debt are just a few of the many factors that influence how we work together." (Adam Bellemare, "Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)

"In Exploiting semantic knowledge graphs can support interpretability and explainability of nearly all AI model types (including DL models) by discovering and depicting semantic and non-obvious relationships or depicting an ML model in a simplified and more readable, explainable way., a Data Mesh solution organizes data around business domain owners and transforms relevant data assets (data sources) to data products that can be consumed by distributed business users from various business domains or functions. These data products are created, governed, and used in an autonomous, decentralized, and self-service manner. Self-service capabilities, which we have already referenced as a Data Fabric capability, enable business organizations to entertain a data marketplace with shopping-for-data characteristics." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)

"Be aware that data product is not the same thing as data as a product. Data as a product describes the idea that data owners treat data as a fully contained product that they are responsible for, rather than a byproduct of a process that others manage, and should make the data available to other domains and consumers. Data product refers to the architecture of implementing data as a product." (James Serra, "Deciphering Data Architectures", 2024)

"Data Mesh advocates for domain-driven ownership of data, enabling individual teams to manage and share their data effectively while aligning with the overall organizational objectives. By embracing this paradigm, enterprises can gradually dismantle the barriers of data silos, paving the way for a more integrated, agile, and data-centric organizational culture." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"[...] Data Mesh introduces agility into the data landscape, emphasizing decentralized ownership, responsive data management, and collaborative cross-functional teams. Just as Agile promotes self-organizing teams, Data Mesh advocates for domain-oriented decentralized ownership, putting the power of data in the hands of individual domain teams. In an Agile context, customer collaboration involves continuous engagement with stakeholders to understand their evolving needs. Likewise, Data Mesh encourages domain teams to engage with data consumers within their organization, gathering feedback, and iterating on their data products to meet their specific requirements." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"[...] Data Mesh is, then, at its foundation, a conceptual framework in the realm of data architecture, which emphasizes decentralized data ownership and architecture. It recognizes that in large organizations, data is vast and varied, where each business domain has autonomy over its own data. By decentralizing control, it empowers individual domains to manage and make decisions about their data while maintaining a cohesive overall structure. And presumably, with this autonomy comes better, more localized, and faster decisions, which in-turn, leads to speed and agility." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Federation is about providing autonomy to each data product owner to make their own decisions about the storage, computing, and sharing of data. However, this autonomy cannot come at a risk to the security and compliance standards of the company." (Aniruddha Deswandikar, "Engineering Data Mesh in Azure Cloud", 2024)

"First, good data products align to decentralized domain ownership - they should align to a domain (large or small) with a clear boundary, and should have an empowered owner. Second, good data products are treated as their name implies - as a product and not a project. Good data products, like other products, have a life cycle, have clear consumers, and a clear value proposition. Good data products are self-serve, meaning that users can get what they need from a data product without undue participation from third parties. Good data products also have a federated governance mechanism, that is to say, that provides for local autonomy and decision making - at the data product level by the data product owner and their team. It also means that while being federated, data product owners/teams are responsible and accountable to ensuring their data product adheres to enterprise guidelines and standards as needed." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Promoting domain-oriented ownership is to combat the common problem of organizational silos. Silos can significantly hinder the free flow of data and expertise, making decision-making and innovation more challenging. We aim to break down these barriers by advocating for domain-oriented ownership and creating a more dynamic and collaborative data management landscape." (Pradeep Menon, "Data Mesh Principles, patterns, architecture, and strategies for data-driven decision making", 2024)

"The role of an empowered owner is multifaceted. They are tasked with ensuring that the data product aligns with both specific business requirements and the overarching governance framework. This alignment is crucial for maintaining the integrity and usefulness of the data product, ensuring it remains a valuable asset within the organization’s data landscape." (Jean-Georges Perrin & Eric Broda, "Implementing Data Mesh: Principles and Practice to Design, Build, and Implement Data Mesh", 2024)

"Data mesh fundamentally reframes data governance and validation by distributing accountability to domain-oriented teams who act as custodians and producers of their respective data products. These teams possess intimate domain knowledge, which is essential for nuanced validation criteria that adapt to the semantics, context, and evolution of their datasets. By treating datasets as first-class products with clear ownership, interfaces, and service-level objectives, data mesh encourages autonomous validation workflows embedded directly within the domains where data originates and is consumed." (William Smith, "Great Expectations for Modern Data Quality: The Complete Guide for Developers and Engineers", 2025)

🤖Prompt Engineering: Parameters (Just the Quotes)

"Fine-tuning involves training the LLM on a smaller, task-specific dataset to adjust its parameters for the specific task at hand. This allows the LLM to leverage its pre-trained knowledge of the language to improve its accuracy for the specific task. Fine-tuning has been shown to drastically improve performance on domain-specific and task-specific tasks and lets LLMs adapt quickly to a wide variety of NLP applications." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"Transfer learning is a technique used in machine learning to leverage the knowledge gained from one task to improve performance on another related task. Transfer learning for LLMs involves taking an LLM that has been pre-trained on one corpus of text data and then fine-tuning it for a specific 'downstream' task, such as text classification or text generation, by updating themodel’s parameters with task-specific data." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"As the tech industry moves from non-generative models to generative models, it is shifting away from feature engineering, or creating features to model the data and experimenting with different hyperparameters to optimize performance. Generative models, and specifically LLMs, do not require feature engineering. Today, the core requirements are usually prompt engineering or building a RAG pipeline - skills that lie within the domain of AI engineers." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Data drift manifests in several distinct ways. Input drift typically shows up as an increase in adversarial or malformed queries that deviate from the original training or design expectations. This can stress the system’s robustness and degrade output quality. Retriever drift occurs when the relevance of the documents returned by retrieval components declines, even if the retrieval algorithms and configurations remain unchanged. Similarly, embedding drift arises when the vector representations used to compare semantic similarity become less effective, causing retrieval systems to fail despite stable system parameters." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"LLM deployment failures often trace back not to the model itself, but to the prompts it receives. In production environments, prompts are rarely fixed, handcrafted snippets. Instead, they are dynamically generated, assembled from templates, and parameterized based on upstream data sources or evolving user state. This dynamism introduces complexity and variability that can subtly undermine the system’s performance if not carefully managed." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"In RAG methods, the AI model itself doesn’t actually 'remember' or learn the proprietary data directly. Instead, the proprietary data are stored separately in what’s called a vector database. When the model is asked a question, it first performs a quick search of the proprietary database, finds relevant pieces of information, and then uses these to generate its response. The model’s core parameters remain entirely unchanged and are never updated with this private data. In this sense, the model hasn’t learned or 'seen' your proprietary data in its internal parameters, it only temporarily consults it as a reference to formulate an answer." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"In 'traditional' ML, where models are predominantly trained for single problems like classifications or regressions, models require extensive training to adapt their weights or parameters to a new task. But you can use LLMs for a wide variety of tasks by changing just the prompt without changing the weights. Foundational models’ capability to adapt to new tasks based solely on examples or instructions they’re given in a prompt is called in-context learning. It emerges from training on diverse datasets that include various task formats and instructions. When given examples of a task within a prompt, the model identifies the underlying pattern and applies it to new instances. This process doesn’t modify the model’s weights, but rather, it leverages the model’s existing knowledge to perform new tasks." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"[...] LLMs raise serious concerns about ethics, bias and fairness, errors in reasoning, hallucinations, and misuse (e.g., misinformation and disinformation). These concerns are exacerbated by modern LLMs being both literal and figurative 'black boxes': Literal black boxes because many advanced AI systems are proprietary and the weights (trained parameters of the models) are not released to the public; and figurative black boxes because even the open-source AI models are so complicated that understanding them and developing safety guardrails has thus far proven extremely difficult." (Mike X Cohen,"50 ML Projects To Understand LLMs", 2026)

"RAG is a paradigm that combines the strengths of LLMs with the rich, often unstructured data stored in a lakehouse. Rather than asking an LLM to generate responses purely from its internal parameters and training data, where knowledge can be outdated or incomplete, RAG systems first retrieve relevant documents, records, or data slices from your lakehouse and then feed those pieces into the model as context for its generative step. The result is an AI that can speak confidently about the latest reports, proprietary datasets, or domain-specific knowledge you have stored without having to retrain the model each time your data changes." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026

"The foundational premise of RAG is that the most effective way to reduce hallucinations is to provide the LLM with the correct, explicit information needed to answer a query, thereby minimizing its need to rely on fallible parametric knowledge. Therefore, the quality, relevance, and accuracy of the retrieval step are the most significant factors in determining the truthfulness of the final output. Better retrieval is the most powerful antidote to hallucination. If the retriever fails to find the correct information, the generator is essentially left to guess, making hallucinations almost inevitable. The goal is to create a tight, unambiguous link between the user’s question and the evidence in the knowledge base." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"Traditional NLP systems rely on static, pretrained knowledge embedded within their parameters, limiting their responses to information available during training. In contrast, RAG systems dynamically access external knowledge bases in real time, enabling them to provide up-to-date and contextually relevant answers. This fundamental distinction leads to key differences in architecture, performance, and adaptability." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"Transformers dominate language for several reasons: their autoregressive framework provides a straightforward training objective (predict next token), self-attention captures long-range dependencies across thousands of tokens, parallel training processes entire sequences simultaneously (unlike RNNs’ sequential bottleneck), and scaling behavior produces emergent capabilities at billions of parameters. These properties make transformers uniquely suited for the language understanding and generation tasks central to enterprise AI applications [...]" (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

🤖Prompt Engineering: Embedding (Just the Quotes)

"Data drift manifests in several distinct ways. Input drift typically shows up as an increase in adversarial or malformed queries that deviate from the original training or design expectations. This can stress the system’s robustness and degrade output quality. Retriever drift occurs when the relevance of the documents returned by retrieval components declines, even if the retrieval algorithms and configurations remain unchanged. Similarly, embedding drift arises when the vector representations used to compare semantic similarity become less effective, causing retrieval systems to fail despite stable system parameters." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"LLM-centric workloads change everything. Now the raw material is heterogeneous text, code, images, audio, and chat logs whose value depends on semantic richness - that is, the informational value of the content - rather than a rigid structure. Pipelines must tokenize, chunk, embed, and version this content; store it in vector indexes for similarity search; and apply filters for personally identifiable information, toxicity, and licensing constraints. Instead of ETL jobs, teams run continuous ingestion and re-embedding loops so that RAG systems stay fresh, and they log every prompt–response pair so that the inputs and outputs can be evaluated and improve the future performance of this system. Data quality in this context is judged by grounding, factuality, and bias metrics - attributes that require automated red-teaming and human-in-the-loop (HITL) review rather than the data structure violation checks of the past." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Semantic Kernel is a framework designed to simplify integrating LLMs into applications that require dynamic knowledge, reasoning, and state tracking. It’s particularly useful when you want to build complex, modular AI systems that can interact with external APIs, knowledge bases, or decision-making processes. Semantic Kernel focuses on building more flexible AI systems that can handle a variety of tasks beyond just generating text. It allows for modularity, enabling developers to easily combine different components - such as embeddings, prompt templates, and custom functions - in a cohesive manner." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"The main difference between unsupervised and supervised embedding methods essentially lies in the task they attempt to solve. Indeed, if unsupervised shallow embedding algorithms try to learn a good graph, node, or edge representation in order to understand the underlying structure, the supervised algorithms try to find the best solution for a prediction task such as node classification, label prediction, or graph classification." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Vector databases are designed to store and index high-dimensional embeddings - dense numeric vectors that capture the semantic meaning of text, images, audio, or other content. Instead of looking for exact matches, they use approximate nearest neighbor (ANN) algorithms to return the items whose vectors lie closest to a query vector in that multidimensional space. This makes them the engine behind semantic search, recommendation systems, image-or-audio similarity matching, and retrieval augmented generation (RAG) pipelines that supply LLM prompts with relevant context in milliseconds." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Embeddings are mathematical representations of text, images, or other input types. They capture semantic meaning by mapping words, phrases, or documents to vectors in a high-dimensional space. LLMs have become exceptionally proficient at creating semantically representative embeddings because this capability forms the foundation for LLM generation. [...] While the latest LLMs provide massive context windows, encoding entire documents in a single embedding vector is often not advisable. You’ll lose the nuances of the different parts of a text, lose critical information, and hinder the RAG system from retrieving relevant information. Plus, it gets very expensive to use large context windows at scale. It’s preferable to chunk documents into information-dense and self-consistent chunks of text." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"RAG is a technique that combines the ability to make use of specialized domain knowledge with the techniques of prompt engineering. In a RAG application, documents that the application should have access to are split into chunks, such as overlapping blocks of text, and embedded as high-dimensional vectors. [...] A RAG application then stores the embeddings created from the documents along with metadata about each embedding, including a link to its source document and position in the document, in a vector database. When a user submits a prompt to an application with RAG capabilities, the application encodes the user’s query using the same embedding model used to encode the documents and finds the closest matches (aka nearest neighbors) between the user prompt and the chunks in the vector database. Finally, a new prompt is created that combines the retrieved snippets of text with a templated instruction. This new prompt is sent to the LLM for inference." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)

"Sentence embeddings are dense vector representations of entire sentences or phrases, capturing their semantic meaning in a fixed-dimensional space. Unlike word embeddings, which represent individual words, sentence embeddings are designed to encode the meaning of longer text sequences. Two prominent techniques for generating sentence embeddings are SBERT(sentence-BERT) and dense retriever. [...] SBERT is a modification of the BERT architecture specifically designed for generating sentence embeddings. Traditional BERT outputs contextualized word embeddings, but SBERT fine-tunes BERT to produce fixed-size sentence embeddings by applying a pooling operation (e.g., mean pooling, max pooling, or CLS token pooling) over the token embeddings." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"The foundational form of RAG, often called naive RAG, follows a straightforward pattern. A pipeline retrieves supporting context from external sources such as enterprise documents, knowledge bases, or structured datasets and appends that information to the model’s prompt before inference. In the most common implementation, each document is converted into an embedding, a numerical representation of its semantic meaning, using either the same foundation model or a specialized embedding model. When a user submits a query, the system performs a vector similarity search to find documents whose embeddings most closely match the query’s vector representation, and the retrieved content is concatenated with the user query before being passed to the language model." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

"The idea behind a GraphRAG is that, having discovered a chunk that has a partial answer to your question, you can retrieve related chunks. This can help you round out the context when each chunk is small. You can also index a document with a small chunk size and then store the embeddings in a tree form so that the embedding of a parent node captures the holistic meaning of all the nodes below it. You can even repeat nodes within the RAG if they make sense in different contexts. You can also index the nodes on both keywords and on embeddings to permit different types of retrieval for different types of queries. In addition, you can incorporate query-focused summarization into a GraphRAG by pregenerating and storing summaries for all groups of closely related entities." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026) 

🤖Prompt Engineering: Reliability (Just the Quotes)

"Agentic intelligence feels incredibly powerful in demos but breaks in production. Indeed, it is very fragile without solid infrastructure. Every day, I personally see tons of clever orchestrations around dumb prompt chains tied up in a brittle, underused LLMOps infrastructure. But building this infrastructure means acknowledging the costs: performance overhead, strict interface contracts, and state complexity, as well as a need for more LLMOps engineers to create the best practices, tooling, and frameworks to run these systems reliably, safely, and robustly." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Despite their impressive capabilities, LLMs are not without limitations. One of the most significant challenges is the problem of hallucination, where an LLM generates factually incorrect or misleading information that appears plausible. This is particularly problematic in domains requiring high factual accuracy, such as healthcare, finance, and legal applications. To mitigate hallucinations and enhance the reliability of LLM outputs,  Retrieval-Augmented Generation (RAG) has emerged as a powerful technique. RAG works by dynamically retrieving relevant information from an external knowledge source (such as a knowledge graph) at inference time, rather than just relying on pre-trained knowledge. This approach ensures that the model has access to up-to-date and accurate data, grounding answers in verified information rather than generating content purely from its internal representations." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"It’s essentially a sophisticated prediction system. Instead of looking up stored answers, an LLM calculates probabilities to determine what text should come next. While these predictions are often accurate, they’re still predictions - which is why it’s crucial to verify any code or factual claims the model generates. This probabilistic nature makes LLMs powerful tools for generating text and code but also means they can make mistakes, even when seeming very confident. Understanding this helps set realistic expectations about what these tools can and cannot do reliably."  (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"A 'hallucination' in the context of generative AI refers to the phenomenon where a model produces information that is factually incorrect, nonsensical, or not grounded in its input data or pre-existing knowledge. These are not mere typos or minor inaccuracies; they are confident, coherent, and often persuasive fabrications. In high-stakes domains like healthcare, law, or finance, a single hallucination can have severe consequences, eroding user trust and leading to catastrophic decision-making. While all LLMs are prone to this, the RAG architecture is specifically designed to combat it by tethering the model’s output to an external, verifiable knowledge base. Understanding why hallucinations occur is the essential first step to building more reliable and truthful AI systems." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026) 

"Ensuring that a language model reliably retrieves and presents correct information, often referred to as factual recall, is critical for any production-grade application. Whether you’re building an internal helpdesk assistant, a medical Q&A system, or an automated compliance auditor, users expect concise, accurate answers that align with up-to-date source material. Unfortunately, without explicit context, even the most powerful LLM can hallucinate or omit key facts." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

"GenAI is so powerful and easy to use that even nontechnical users can easily prototype very compelling applications on top of GenAI. However, taking such GenAI prototypes to production is hard because GenAI models are unreliable - they can hallucinate, return different answers to the same input, and can have surprising limitations because of how they are trained." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"KGs are sophisticated graph structures that represent real-world entities (people, places, diseases, proteins), define meaningful connections between them, and provide context. KGs provide structured, explainable knowledge representation but are challenging to build and query; LLMs offer natural language processing capabilities but suffer from hallucinations, stale information, and a lack of domain-specific grounding. Together, they are a 'killer combination': LLMs can extract entities and relationships from unstructured text to build KGs more efficiently, providing more autonomous and powerful graph querying and analysis. Meanwhile, KGs provide reliable, up-to-date domain knowledge to ground LLM responses and prevent hallucinations." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)

"Misinformation is false or misleading information, and its generation by AI is particularly dangerous because of the aura of credibility these systems can project. In a RAG system, misinformation primarily arises from two failure points: Retrieval of inaccurate content from the knowledge base and fabrication or distortion by the large language model (LLM) during generation, even when given good context. The first line of defense is ensuring the integrity of the knowledge base. A RAG system is only as reliable as the documents it has access to. If non-credible, manipulated, or satirical sources are ingested, the system will retrieve and use them as fact. This makes rigorous data curation and source validation the most critical step in combating misinformation. The second line of defense is strengthening the connection between retrieval and generation to prevent the LLM from 'going off script'. The LLM, based on its pre-trained knowledge, might confidently generate an answer that contradicts the provided evidence or adds unsupported details – a phenomenon known as 'hallucination'. To mitigate this, thesystem must be designed to strictly adhere to the retrieved context." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"[...] RAG models excel in dynamic environments where information changes frequently, such as news generation or customer support. Standard generative models, constrained by their training data, may provide outdated or irrelevant responses. RAG, however, can pull the latest information, ensuring up-to-date and contextually appropriate outputs. While RAG models may require more computational resources due to the retrieval step, the trade-off is often justified by the substantial improvements in accuracy and reliability, making them a superior choice for many real-world applications." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"RAG models offer several advantages over standard generative models, addressing many of their limitations. Traditional generative models, like GPT, rely solely on patterns learned during training, which can lead to issues such as factual inaccuracies, model hallucinations, and lack of contextual relevance. These models generate content based on pre-existing knowledge, often without the ability to verify or update the information, making them less reliable for tasks requiring high accuracy. In contrast, RAG models integrate retrieval mechanisms that allow them to access external knowledge sources in real time. This ensures that the generated content is grounded in verified data, significantly improving factual consistency and relevance. For example, while a standard generative model might produce a plausible but incorrect answer to a factual question, a RAG model can retrieve and incorporate accurate information from a trusted source, reducing errors." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

🤖Prompt Engineering: Tuning (Just the Quotes)

"Different algorithms may perform better on different types of text data and will have different vector sizes. The choice of algorithm can have a significant impact on the quality of the resulting embeddings. Additionally, open-source alternatives may require more customization and finetuning than closed-source products, but they also provide greater flexibility and control over the embedding process." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"Fine-tuning involves training the LLM on a smaller, task-specific dataset to adjust its parameters for the specific task at hand. This allows the LLM to leverage its pre-trained knowledge of the language to improve its accuracy for the specific task. Fine-tuning has been shown to drastically improve performance on domain-specific and task-specific tasks and lets LLMs adapt quickly to a wide variety of NLP applications." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"LLMs are advanced natural language processing (NLP) deep learning models. They specialize in both processing contextual language at scale and predicting the likelihood of a sequence of tokens in a specific language. The smallest units of semantic meaning, tokens can be words or sub-words and act as the key inputs for an LLM. LLMs can be categorized as autoregressive, autoencoding, or a combination of both. Their defining feature is their substantial size, which enables them to execute complex language tasks like text generation and classification, with high precision and potentially minimal fine-tuning." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)  

"LLMs encode information directly into their parameters via pre-training and fine-tuning, but keeping them up to date with new information is tricky. We either have to further fine-tune the model on new data or run the pre-training steps again from scratch." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024) 

"Transfer learning is a technique used in machine learning to leverage the knowledge gained from one task to improve performance on another related task. Transfer learning for LLMs involves taking an LLM that has been pre-trained on one corpus of text data and then fine-tuning it for a specific 'downstream' task, such as text classification or text generation, by updating the model’s parameters with task-specific data. The idea behind transfer learning is that the pre-trained model has already learned a lot of information about the language and relationships between words, and this information can be used as a starting point to improve performance on a new task. Transfer learning allows LLMs to be fine-tuned for specific tasks with much smaller amounts of task-specific data than would be required if the model were trained from scratch. This greatly reduces the amount of time and resources needed to train LLMs." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)  

"The pre-training step is by far the biggest in terms of data and compute requirements for the whole of the LLM’s lifecycle. Yet fine-tuning is quite resource-intensive when we compare it to traditional machine learning and deep learning workflows. Fine-tuning is also a very important step in improving the quality of the models; hence, it makes sense to understand how we can optimize this step without impacting the performance. Efficiencies in this step also enable us to iterate faster, thereby improving adaptability in many fast-moving domains. In this section, we will focus on some interesting efficient method." (Joseph Babcock & Raghav Bali, "Generative AI with Python and PyTorch" 2nd. Ed., 2025)

 "There are three techniques for model domain adaptation: prompt engineering, RAG, and fine-tuning. Strictly speaking, RAG is a form of dynamic prompt engineering where developers use a retrieval system to add content to an existing prompt, but RAG systems are used so often that it’s worth discussing them separately. One critical difference with fine-tuning is that you must have access to the model’s weights, information that is usually not available with cloud-based, proprietary LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Before releasing the fine-tuned model to a production environment, you need to evaluate its performance and make sure it hasn’t picked up any counterproductive constructs, like biased language. Also, whenever a new version of your foundational model is released, you must fine-tune the model again and redo the training. Finally, you need to track the lineage of the training and validation data used to train the fine-tuned model. These are additional tasks that require careful execution, and using in-context learning is far simpler since it only requires you to add a handful of examples." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Content Optimization is a pattern that uses preference tuning to produce optimally performing content. Preference tuning is a way of training an LLM on a dataset of paired items so that it outputs responses that are closer to the chosen items than the rejected ones. Unlike optimization methods, in which optimization attempts to approximate an independent source of truth, this pattern attempts to hack the model to create content the evaluation method will consider great." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Fine-tuning LLMs can lead to catastrophic forgetting, in which the model overemphasizes the examples provided during fine-tuning and loses its previously acquired knowledge. This wipes out LLMs’ primary advantage: their comprehensive world knowledge. You can mitigate this issue by fine-tuning your model on a small dataset, for only a few epochs, and by selecting an appropriate learning rate during the fine-tuning process. Generally, you should begin the fine-tuning learning rate at the value where the pretraining phase concluded (typically around 1e-5)." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"The type of post-training you can do is intimately connected to the structure of the dataset. If the dataset consists purely of text completions, then you can use it to do unsupervised training, such as teaching a model new vocabulary or completely new associations via CPT. If the dataset consists of ideal responses to inputs (input-output pairs), then you can use it for SFT or instruction tuning. If the dataset consists of two responses to each input and notes which of the two is preferable, then you can use it for preference tuning. You can do any of these forms of post-training in a parameter efficient way or on quantized models." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

🖍️Valliappa Lakshmanan - Collected Quotes

"A RAG system needs to identify relevant text chunks from the knowledge base. It does that by building a datastore of chunks and organizing the chunks in a way that makes searching for chunks that are relevant to a query efficient and fast. This preparatory step of building an efficient data store is called indexing, and the runtime step of searching the datastore for relevant chunks is called retrieval. [...] Whenever a query is received, a question-answering pipeline is invoked This consists of two steps: retrieval and generation. In the retrieval step, the retriever finds chunks in the document store that are the most relevant to the query. In the generation step, these relevant chunks are added to the context of the prompt and used along with the user query to generate the answer. In this way, the generated answer is grounded in the relevant information from trusted knowledge sources. The response consists of the generated text as well as the relevant chunks that were used to generate the answer." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"As an AI engineer, you can ask a foundational model to directly generate the content your application needs by sending the model an appropriate text input, which is known as a prompt. However, you will face certain common problems - the generated content may not match the style you want, may be missing enterprise knowledge that the model doesn’t know about, or may lack certain capabilities." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Authentication and authorization are important parts of any communication between clients and servers. We need to have a clear separation between different roles and to be able to specify what capabilities a particular client is allowed to invoke. MCP, however, is more of a language interoperability protocol - it doesn’t enforce any security principles. [...] Collaboration In MCP, communication between the client and the server is mostly one way, whereas in the real world, you may want collaborative, interactive workflows." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Before releasing the fine-tuned model to a production environment, you need to evaluate its performance and make sure it hasn’t picked up any counterproductive constructs, like biased language. Also, whenever a new version of your foundational model is released, you must fine-tune the model again and redo the training. Finally, you need to track the lineage of the training and validation data used to train the fine-tuned model. These are additional tasks that require careful execution, and using in-context learning is far simpler since it only requires you to add a handful of examples." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Content Optimization is a pattern that uses preference tuning to produce optimally performing content. Preference tuning is a way of training an LLM on a dataset of paired items so that it outputs responses that are closer to the chosen items than the rejected ones. Unlike optimization methods, in which optimization attempts to approximate an independent source of truth, this pattern attempts to hack the model to create content the evaluation method will consider great." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Embeddings are mathematical representations of text, images, or other input types. They capture semantic meaning by mapping words, phrases, or documents to vectors in a high-dimensional space. LLMs have become exceptionally proficient at creating semantically representative embeddings because this capability forms the foundation for LLM generation. [...] While the latest LLMs provide massive context windows, encoding entire documents in a single embedding vector is often not advisable. You’ll lose the nuances of the different parts of a text, lose critical information, and hinder the RAG system from retrieving relevant information. Plus, it gets very expensive to use large context windows at scale. It’s preferable to chunk documents into information-dense and self-consistent chunks of text." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Fine-tuning LLMs can lead to catastrophic forgetting, in which the model overemphasizes the examples provided during fine-tuning and loses its previously acquired knowledge. This wipes out LLMs’ primary advantage: their comprehensive world knowledge. You can mitigate this issue by fine-tuning your model on a small dataset, for only a few epochs, and by selecting an appropriate learning rate during the fine-tuning process. Generally, you should begin the fine-tuning learning rate at the value where the pretraining phase concluded (typically around 1e-5)." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Foundational models are closed systems that are limited by their training data. In many cases, you’ll need to give a foundational model additional information. For example, the information may be based on recent events that had not occurred when the foundational model was being trained, or the information may have been private, confidential, or otherwise unavailable to the foundational model trainers. It’s impractical to retrain an LLM with additional knowledge or even perform continuing pretraining (CPT) on a foundational model to add knowledge to it. The cost of even a single training run is significant, and information changes so fast that CPT would have to be done very frequently." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Foundational models can manipulate symbols and words effectively, but this may be because they are generalizing from the manipulation they have encountered in the training data, not because they grasp semantic meanings and the logical relationships between them in the way humans do. While you can use foundational models to perform many tasks, that’s because those tasks are similar to the tasks that the foundational model was trained to do." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"GenAI is so powerful and easy to use that even nontechnical users can easily prototype very compelling applications on top of GenAI. However, taking such GenAI prototypes to production is hard because GenAI models are unreliable - they can hallucinate, return different answers to the same input, and can have surprising limitations because of how they are trained." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"In 'traditional' ML, where models are predominantly trained for single problems like classifications or regressions, models require extensive training to adapt their weights or parameters to a new task. But you can use LLMs for a wide variety of tasks by changing just the prompt without changing the weights. Foundational models’ capability to adapt to new tasks based solely on examples or instructions they’re given in a prompt is called in-context learning. It emerges from training on diverse datasets that include various task formats and instructions. When given examples of a task within a prompt, the model identifies the underlying pattern and applies it to new instances. This process doesn’t modify the model’s weights, but rather, it leverages the model’s existing knowledge to perform new tasks." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Multiagent architectures offer better scalability than monolithic approaches do. Rather than scaling vertically by increasing model size, multiagent systems can scale horizontally by adding more specialized agents as needed. You can update or replace individual agents without having to retrain the entire system, and that allows for more agile development and deployment. Computational resources can also be allocated dynamically based on current needs, with more resources directed to the most critical or computationally intensive subtasks. Multiagent systems are also more robust. Critical capabilities can be replicated across multiple agents to reduce the impact of individual agent failures. Different agents can approach problems with different methodologies, which increases the likelihood of finding effective solutions. Agents can verify each other’s outputs to identify and correct errors that might go undetected in a single-agent system." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Prompt engineering may meet your accuracy requirements, but the prompts end up being extremely long as you encounter more and more corner cases. It’s not unusual for such prompts to amount to two or three pages of detailed instructions. The complex system instructions required to achieve that level of accuracy incur a large token count, and that makes the application too expensive to scale. [...] As your prompts become more detailed, maintenance overhead increases because the overall system is extremely sensitive to the specific wording of prompts. You’ll need to do integration testing every time the foundational model changes, and often even when one of your tools changes." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"The Chain of Thought pattern uses chain-of-thought (CoT) reasoning, which is a prompting technique that enhances LLMs’ problem-solving capabilities by encouraging them to break down complex problems into intermediate reasoning steps before arriving at a final answer. [...] CoT involves modifying the prompting strategy - instead of asking for a direct answer, the prompt explicitly requests or demonstrates a step-by-step reasoning process. There are three variants: Zero-shot CoT, Few-shot CoT, and Auto-CoT. [...] In Zero-shot CoT, you request a step-by-step reasoning by simply adding the phrase think step-by-step to the prompt. [...] In cases where the model’s pretrained capabilities do not include the specific logic needed, you can try Few-shot CoT, which involves providing the model with examples of step-by-step reasoning for similar problems before asking it to solve a new problem. In Few-shot CoT, you demonstrate the desired reasoning pattern through examples. [...] In the Auto-CoT variant, you add demonstrative examples to a wide variety of questions to a database called an example store. Most people use a document database (in which the examples are indexed by keywords in the question text) or a vector database (in which the examples are indexed by the embedding of the questions) for the example store." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"The Deep Search pattern involves an iterative loop that consists of retrieval and thinking until either a good enough answer is found or the time/cost budget is exhausted. It goes beyond traditional RAG systems in three ways. First, it adds a thinking step between retrieval and generation to determine what additional information, if any, needs to be gathered. Second, instead of a single retrieval step, Deep Search involves iterating through multiple stages of retrieval and generation. Finally, instead of just relying on adding knowledge from an enterprise knowledge base, Deep Search employs tools such as search engines and enterprise APIs to gather the knowledge it needs proactively." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)  

"The idea behind a GraphRAG is that, having discovered a chunk that has a partial answer to your question, you can retrieve related chunks. This can help you round out the context when each chunk is small. You can also index a document with a small chunk size and then store the embeddings in a tree form so that the embedding of a parent node captures the holistic meaning of all the nodes below it. You can even repeat nodes within the RAG if they make sense in different contexts. You can also index the nodes on both keywords and on embeddings to permit different types of retrieval for different types of queries. In addition, you can incorporate query-focused summarization into a GraphRAG by pregenerating and storing summaries for all groups of closely related entities." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"The Reflection pattern is an agentic approach in which an AI system evaluates its own reasoning, decisions, and outputs before finalizing them. This self-monitoring process allows the system to identify errors, improve solutions, and refine its approach to problems. Here, the AI system as a whole is reflecting and improving, and the foundational model used to generate the response remains the same. You’ll sometimes see this process referred to as self-reflection, but there’s no reason why the evaluation has to be done by an LLM at all, let alone by the same LLM that generates the content."  (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"The type of post-training you can do is intimately connected to the structure of the dataset. If the dataset consists purely of text completions, then you can use it to do unsupervised training, such as teaching a model new vocabulary or completely new associations via CPT. If the dataset consists of ideal responses to inputs (input-output pairs), then you can use it for SFT or instruction tuning. If the dataset consists of two responses to each input and notes which of the two is preferable, then you can use it for preference tuning. You can do any of these forms of post-training in a parameter efficient way or on quantized models." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"Traditional RAG systems 'guide' LLMs in generating responses by providing document chunks that are related to the user’s initial query. Incorporating external knowledge from a document store helps to reduce hallucinations, but its effectiveness relies heavily on the relevance of the retrieved document chunks. This can be problematic if the retrieval step produces irrelevant document chunks or incorrect information. The key goal of corrective retrieval-augmented generation (CRAG) is to detect and correct hallucinations before the LLM attempts response generation. It adds an evaluator to the process that evaluates the quality of the retrieved chunks for a given query before passing on the documents to the response generation step. The evaluator returns a confidence score that triggers different knowledge retrieval actions." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"What is an agent? In computer science, the term agent has long been used to describe software entities that act on behalf of users or other programs. When you invoke a foundational model, you specify a role, provide some context, and ask it to carry out some instruction. In the computer science sense, then, the LLM is acting as your agent." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)

"When a RAG system generates a response, the generation can blur the boundaries of the original chunks or multiple chunks can contribute to a single statement. Some LLMs tend to paraphrase chunks in ways that will alter the direct connection to the source chunk, and the paraphrasing can make direct text matching difficult." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)