🤖Prompt Engineering: Trust (Just the Quotes)

"Chain-of-thought prompting is a method that forces LLMs to reason through a series of steps, resulting in more structured, transparent, and precise outputs. The goal is to break down complex tasks into smaller, interconnected subtasks, allowing the LLM to address each subtask in a stepby-step manner. This not only helps the model to 'focus' on specific aspects of the problem, but also encourages it to generate intermediate outputs, making it easier to identify and debug potential issues along the way. Another significant advantage of chain-of-thought prompting is the improved interpretability and transparency of the LLM-generated response. By offering insights into the model’s reasoning process, we, as users, can better understand and qualify how the final output was derived, which promotes trust in the model’s decision-making abilities." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"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)

"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

"As AI systems become more integrated into critical decision-making processes, the 'black box' problem – the inability to understand how a model arrived at a specific output – becomes a major barrier to trust and adoption. For RAG systems, this is particularly crucial; a user needs to know not just the answer, but why the system believes that answer to be true. Transparency and explainability (explainable AI, XAI) are the disciplines focused on making AI reasoning understandable to humans. A transparent RAG system allows users to verify the accuracy of its responses, builds trust by demonstrating a logical process, and enables developers to debug and improve the system. It transforms the AI from an oracle that must be blindly trusted into a tool for augmented intelligence, where the human remains the ultimate arbiter of truth." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"Generative AI has a lot of problems because it needs a lot of data to work. Generative models like GPT and GANs need a lot of training data to learn patterns and make good outputs. This data dependency can cause problems like overfitting, which happens when the model does well on training data but doesn’t work well with new, unseen data. Also, the quality of the content that is generated is directly related to the diversity and representativeness of the training data. This means that biased or incomplete datasets can lead to outputs that are wrong or unfair." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"If ethical lapses or AI failures occur, the impact on a business can be significant. Misinformation, biases, or harmful content generated by AI can lead to reputational damage, customer distrust, and potential regulatory scrutiny. The public relations fallout from an AI-driven error or ethical misstep can erode consumer confidence, resulting in lost revenue and lasting harm to brand image. Businesses, therefore, need to proactively address ethical considerations in AI implementation, not only to ensure compliance but also to protect and strengthen their reputation in a highly competitive, and increasingly transparent, marketplace." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 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 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)

"The power of RAG systems stems from their ability to access and process vast amounts of data. However, this very capability introduces significant risks regarding the privacy of individuals and the security of sensitive information. Unlike a simple chatbot, a RAG system often has access to proprietary corporate data, internal documentation, and potentially personal user information within its knowledge base. A data breach or misuse of this information can lead to severe financial, legal, and reputational damage. Furthermore, a global patchwork of stringent regulations now governs how personal data must be handled, making compliance a central pillar of AI system design, not an afterthought. Ethical deployment requires an architecture built on privacy by design and by default, ensuring user trust is maintained through robust technical and procedural safeguards." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"The promise of AI is its ability to process information objectively and at scale. However, this promise is fundamentally threatened by the twin challenges of bias and misinformation. AI systems are not born in a vacuum; they are created by humans and trained on data produced by humans. Consequently, they are prone to inheriting and even amplifying our prejudices, errors, and the systemic inequalities present in that data. In a RAG system, this risk is a two-fold problem: first in the retrieval of information, and second in the generation of a response based on that retrieval. A failure to address these issues doesn’t just lead to technically incorrect outputs; it can perpetuate social harm, erode public trust, and lead to the widespread dissemination of falsehoods. Therefore, understanding and mitigating bias and misinformation is not an optional add-on but a core requirement for any ethically deployed AI system." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

🤖Prompt Engineering: Attention (Just the Quotes)

"Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations.  End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks. To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution." (Ashish Vaswani et al, "Attention Is All You Need", 2017) [source] 

"Attention is a mechanism used in deep learning models (not just Transformers) that assigns different weights to different parts of the input, allowing the model to prioritize and emphasize the most important information while performing tasks like translation or summarization. Essentially, attention allows a model to 'focus' on different parts of the input dynamically, leading to improved performance and more accurate results. Before the popularization of attention, most neural networks processed all inputs equally and the models relied on a fixed representation of the input to make predictions. Modern LLMs that rely on attention can dynamically focus on different parts of input sequences, allowing them to weigh the importance of each part in making predictions." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"[...] LLMs are pre-trained on large corpora and sometimes fine-tuned on smaller datasets for specific tasks. Recall that one of the factors behind the Transformer’s effectiveness as a language model is that it is highly parallelizable, allowing for faster training and efficient processing of text. What really sets the Transformer apart from other deep learning architectures is its ability to capture long-range dependencies and relationships between tokens using attention. In other words, attention is a crucial component of Transformer-based LLMs, and it enables them to effectively retain information between training loops and tasks (i.e., transfer learning), while being able to process lengthy swatches of text with ease." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"Transformers are complex models built like LEGO blocks using multiple smart and specialized components. [...] Briefly, a vanilla transformer model consists of separate stacks of encoders and decoders. Each encoder block includes multi-head self-attention, enabling the model to capture relationships between tokens regardless of their positions. Residual connections help maintain gradient flow, preventing the vanishing gradient problem. Layer normalization ensures training stability, and feed-forward layers introduce non-linearity and learn complex token interactions. Decoder blocks contain the same components but also include an encoder-decoder attention mechanism to incorporate context from the encoder. The model uses embedding layers to convert tokens into a continuous latent space for contextual learning and positional encoding to preserve the order of tokens in the sequence." (Joseph Babcock & Raghav Bali, "Generative AI with Python and PyTorch" 2nd. Ed., 2025)

"GenAI encompasses a variety of model architectures, including diffusion models, normalizing flows, and autoregressive models. However, three types of GenAI have become particularly dominant in practical applications. GANs pit two neural networks against each other to produce realistic data. VAEs encode input data into a latent space and decode it to generate new samples. Transformer-based models, like those in the GPT, Llama, and Gemini families, leverage attention mechanisms to generate coherent and contextually relevant sequences." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

"The self-attention mechanism is the cornerstone of transformer models, enabling them to process sequential data like text more effectively than traditional recurrent neural networks (RNNs) or convolutional neural networks (CNNs). Unlike RNNs, which process sequences step-by-step, self-attention allows the model to consider the entire input sequence simultaneously. This parallel processing capability makes transformers highly efficient and scalable. transformers highly efficient and scalable. At its core, self-attention computes relationships between all words in a sentence, assigning higher weights to words that are more relevant to each other" (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"Transformer-based models represent the dominant architecture for processing sequential data, particularly text. Unlike GANs and VAEs, which excel at generating visual content, transformers have revolutionized natural language processing (NLP) and are now being applied to code, music, and even protein structures. At their core, transformers use a mechanism called self-attention that allows the model to consider relationships between all elements in a sequence simultaneously. This enables transformers to capture long-range dependencies and contextual nuances that earlier sequential models struggled with." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026) 

"Unlike earlier neural network designs that processed text sequentially, transformers use a mechanism called self-attention that allows the model to consider relationships between all words in a passage simultaneously. This parallel processing enables transformers to capture long-range dependencies and contextual nuances that previous architectures struggled with. Positional encoding provides information about word order, while the attention mechanism allows the model to focus on relevant context when processing each word." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

🤖Prompt Engineering: Context (Just the Quotes)

"First, intelligence is situational - there is no such thing as general intelligence. Your brain is one piece in a broader system which includes your body, your environment, other humans, and culture as a whole. Second, it is contextual - far from existing in a vacuum, any individual intelligence will always be both defined and limited by its environment. (And currently, the environment, not the brain, is acting as the bottleneck to intelligence.) Third, human intelligence is largely externalized, contained not in your brain but in your civilization. Think of individuals as tools, whose brains are modules in a cognitive system much larger than themselves - a system that is self-improving and has been for a long time." (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"Context is crucial for how language models understand and generate code. The model processes your input by analyzing relationships between different parts of the code and documentation to determine meaning and intent. [...] The model evaluates context by calculating mathematical relationships between elements in your input. However, it may miss important domain knowledge, coding standards, or architectural patterns that experienced developers understand implicitly." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"Context manipulation involves setting up an optimal environment within the prompt to help a model generate accurate and relevant responses. By controlling the context in which the model operates, users can influence the output’s quality, consistency, and specificity, especially in tasks requiring clarity and precision. Context manipulation involves priming the model with relevant information, presenting examples within the prompt, and utilizing system messages to maintain the desired behavior." (Jeremy C Morgan, "Coding with AI: Examples in Python", 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)

"LLMs excel at understanding context and making associations among words, phrases, and concepts to provide relevant information based on the input query or prompt. While structured knowledge bases rely on humancurated data, LLMs can  automatically extract knowledge from unstructured text. When trained on diverse textual sources, they can process a vast amount of information without explicit human intervention. However, this also introduces a challenge, as the model can learn biased or incorrect information from the training data." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"RAG is a framework that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs. In this setup, an LLM is augmented with a retrieval component that fetches relevant information from external data sources, such as knowledge bases or databases, to produce more accurate and contextually relevant responses. This method enhances the LLM’s output by grounding it in authoritative, up-to-date information." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"These user-controlled templates are pre-engineered prompt structures that can be presented to the model as part of the context or decision-making path. Prompts help guide the model’s behavior using predefined instructions, formats, strategies. They can encapsulate common workflows suggest best practices for using tools and resourceseffective." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Unlike traditional code completion, which operates on predefined rules, generative AI creates a continuous improvement cycle, which includes the following five basic steps: (1) Developer input: You provide source code, comments, or natural language requirements. (2) Context analysis: The model analyzes patterns in your existingcode and requirements. (3) Prediction: Based on training data and your specific context, the model generates probable code. (4) Developer feedback: You accept, modify, or reject suggestions. (5) Model adaptation: The system incorporates your feedback to improve future suggestions." (Jeremy C Morgan, "Coding with AI: Examples in Python", 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)

"With MCP, a model no longer has to guess what’s possible. Instead, it can discover tools, query data sources, and select prompts - all in real time, all through a shared protocol. This means a model doesn’t just generate responses; it acts, it calls tools, it gathers context, and it learns how to interact with the outside world in a modular, controlled way." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"A major aspect of the alignment problem is values misalignment: AI systems may lack sufficient understanding of human ethics, cultural norms, or social contexts, making it difficult to translate our values directly into algorithms. For instance, if an AI is programmed to prioritise efficiency without balancing safety or ethical considerations, it might make decisions that, while effective, disregard human welfare. This is further complicated by 'specification gaming', where an AI might exploit loopholes in its programming to achieve objectives in unintended or counterproductive ways. An AI instructed to avoid obstacles, for instance, could redefine what counts as an 'obstacle' and take routes that, while technically following the rule, lead to negative outcomes." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"Intelligent systems connect users to AI and ML to achieve meaningful objectives. An intelligent system is one in which intelligence evolves and improves over time, particularly when it improves by watching how users interact with the system.[...] The primary objective of the intelligent system is to support users in accomplishing complex tasks - not by replacing them, but by enhancing their decision-making capabilities. [...] An intelligent system must also have the ability to learn from user interactions and explicit feedback, as well as utilize contextual information. The system should contin-uously develop, use, and maintain an evolving knowledge base. This evolution is driven not only by data sources but also by ongoing interactions with users." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 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)

"Generative artificial intelligence (GenAI), powered by large language models (LLMs) like Google’s Gemini and OpenAI’s GPT, has transformed how we work and live, revolutionizing business after business. Despite this success, generative AI falls short in domains where specific domain knowledge, high accuracy, and explainability are essential. And it has other significant limitations, including hallucinations and a lack of context and relations. This is where knowledge graphs (KGs) come in, provid-ing contextual information - such as experiences, environmental characteristics, cultural aspects, and social normsneeded to build the 'third wave of AI' for mission-critical applications." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)

"MCP gives AI applications a common way to discover available capabilities, retrieve context, and invoke actions across local and remote systems. In a Lakehouse environment, that standardization is especially valuable. [...] With MCP, a host can connect to a server that exposes capabilities such as catalog discovery, schema inspection, SQL execution, or workflow orchestration through a consistent protocol layer. That makes the Lakehouse easier to expose to notebooks, IDEs, assistants, and agent frameworks in a way that is composable, inspectable, and easier to govern." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

"MCP is an open standard that lets AI applications connect to external tools, data sources, and workflows through a consistent, discoverable interface. In practice, MCP enables LLM-powered hosts, such as IDEs, desktops, and cloud services, to discover capabilities at runtime. These hosts can then invoke tools through a shared schema without requiring bespoke adapters for each integration [..]. MCP addresses the long-standing M×N integration problem: without a common protocol, every model ends up integrating separately with every system. MCP collapses this complexity by standardizing how agents discover, call, and exchange context with external capabilities." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 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, the system must be designed to strictly adhere to the retrieved context." (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) 

"Unlike earlier neural network designs that processed text sequentially, transformers use a mechanism called self-attention that allows the model to consider relationships between all words in a passage simultaneously. This parallel processing enables transformers to capture long-range dependencies and contextual nuances that previous architectures struggled with. Positional encoding provides information about word order, while the attention mechanism allows the model to focus on relevant context when processing each word." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

🤖Prompt Engineering: Accuracy (Just the Quotes)

"Attention is a mechanism used in deep learning models (not just Transformers) that assigns different weights to different parts of the input, allowing the model to prioritize and emphasize the most important information while performing tasks like translation or summarization. Essentially, attention allows a model to 'focus' on different parts of the input dynamically, leading to improved performance and more accurate results. Before the popularization of attention, most neural networks processed all inputs equally and the models relied on a fixed representation of the input to make predictions. Modern LLMs that rely on attention can dynamically focus on different parts of input sequences, allowing them to weigh the importance of each part in making predictions." (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)

"Large language models (LLMs) are AI models that are usually (but not necessarily) derived from the Transformer architecture and are designed to understand and generate human language, code, and much more. These models are trained on vast amounts of text data, allowing them to capture the complexities and nuances of human language. LLMs can perform a wide range of language-related tasks, from simple text classification to text generation, with high accuracy, fluency, and style." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024) 

"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)

"Generative AI tools for coding are sometimes inaccurate. They can produce results that look good but are wrong. This is common with LLMs. They can write code or chat like a person. And sometimes, they share information that’s just plain wrong. Not just a bit off, but totally backwards or nonsense. And they say it so confidently! We call this 'hallucinating', which is a funny term, but it makes sense." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"In prompt engineering, we customize the prompts or questions we give the model to get more accurate or insightful responses. The way a prompt is structured has a massive impact on how well a model understands the task at hand and, ultimately, how well it performs. Given LLMs’ versatility, prompt engineering has become an important skill for getting the most out of these models across different domains and tasks. The key is to understand how different prompt structures lead to different model behaviors. There are various strategies - ranging from simple one-shot prompting to more complex techniques like chain-of-thought prompting - that can significantly improve the effectiveness of LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"RAG is a framework that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs. In this setup, an LLM is augmented with a retrieval component that fetches relevant information from external data sources, such as knowledge bases or databases, to produce more accurate and contextually relevant responses. This method enhances the LLM’s output by grounding it in authoritative, up-to-date information." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 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) 

"Another big problem is model hallucinations, which happen when generative models make content that seems real but is actually wrong or made up. For instance, a language model could write a news story or a medical diagnosis that has wrong information. This happens because these models value coherence and fluency more than factual accuracy. When the training data is not enough or is not clear, they often 'fill in the gaps' with made-up information." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"Beyond intentionally misleading content, GenAI systems can produce inaccurate information unintentionally. LLMs are prone to hallucination, generating plausible but false statements with the same confidence as accurate ones. In enterprise contexts, this poses particular risks: an AI assistant might report incorrect financial figures, fabricate customer details, or misrepresent historical trends. Organizations deploying GenAI must implement validation mechanisms, human oversight, and retrieval-augmented approaches that ground model outputs in verified data sources." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 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)

"Generative artificial intelligence (GenAI), powered by large language models (LLMs) like Google’s Gemini and OpenAI’s GPT, has transformed how we work and live, revolutionizing business after business. Despite this success, generative AI falls short in domains where specific domain knowledge, high accuracy, and explainability are essential. And it has other significant limitations, including hallucinations and a lack of context and relations. This is where knowledge graphs (KGs) come in, provid-ing contextual information - such as experiences, environmental characteristics, cultural aspects, and social normsneeded to build the 'third wave of AI' for mission-critical applications." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 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 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)

"Retrieval mechanisms are essential for addressing some of the key limitations of traditional generative AI models, such as factual inaccuracies, lack of context awareness, and model hallucinations. While generative models excel at creating coherent and fluent content, they often struggle to produce outputs that are factually correct or contextually relevant. This is because these models rely solely on patterns learned during training, without access to real-time or external information. For example, a generative model might generate a plausible-sounding but incorrect answer to a factual question, as it cannot verify the accuracy of its response." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 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)

🤖Prompt Engineering: Knowledge Bases (Just the Quotes)

"How can a cognitive system process environmental input and stored knowledge so as to benefit from experience? More specific versions of this question include the following: How can a system organize its experience so that it has some basis for action even in unfamiliar situations? How can a system determine that rules in its knowledge base are inadequate? How can it generate plausible new rules to replace the inadequate ones? How can it refine rules that are useful but non-optimal? How can it use metaphor and analogy to transfer information and procedures from one domain to another?" (John H Holland et al, "Induction: Processes Of Inference, Learning, And Discovery", 1986)

"Inference is the process of matching current facts from the domain space to the existing knowledge and inferring new facts. An inference process is a chain of matchings. The intermediate results obtained during the inference process are matched against the existing knowledge. The length of the chain is different. It depends on the knowledge base and on the inference method applied." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Representation is the process of transforming existing problem knowledge to some of the known knowledge-engineering schemes in order to process it by applying knowledge-engineering methods. The result of the representation process is the problem knowledge base in a computer format." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"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)

"LLMs excel at understanding context and making associations among words, phrases, and concepts to provide relevant information based on the input query or prompt. While structured knowledge bases rely on humancurated data, LLMs can  automatically extract knowledge from unstructured text. When trained on diverse textual sources, they can process a vast amount of information without explicit human intervention. However, this also introduces a challenge, as the model can learn biased or incorrect information from the training data." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"RAG is a framework that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs. In this setup, an LLM is augmented with a retrieval component that fetches relevant information from external data sources, such as knowledge bases or databases, to produce more accurate and contextually relevant responses. This method enhances the LLM’s output by grounding it in authoritative, up-to-date information." (Aldo Marzullo et al, "Graph Machine Learning" 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)

"Intelligent systems connect users to AI and ML to achieve meaningful objectives. An intelligent system is one in which intelligence evolves and improves over time, particularly when it improves by watching how users interact with the system.[...] The primary objective of the intelligent system is to support users in accomplishing complex tasks - not by replacing them, but by enhancing their decision-making capabilities. [...] An intelligent system must also have the ability to learn from user interactions and explicit feedback, as well as utilize contextual information. The system should contin-uously develop, use, and maintain an evolving knowledge base. This evolution is driven not only by data sources but also by ongoing interactions with users." (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)

"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)

🤖Prompt Engineering: Failure (Just the Quotes)

"Agentic workflows break when the logic is messy - if, say, the plans don’t decompose or memory is poorly structured. However, infrastructure-level LLM applications introduce even more failure points and complexity. If the protocols don’t sync with each other, or the data flows start leaking, or the model boundaries are unclear... there are far too many failure points to count. While most people have been jumping on the bandwagon to adopt MCPs or A2A, very few are equipped to handle the LLMOps issues these tools introduce." (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)

"The simplest form of an agent is little more than a wrapped prompt. It takes an input, does some local reasoning, returns an output, and exits. There’s no memory, no iteration, no feedback loop. These are useful when the task is bounded, like generating a SQL query, converting a paragraph to a tweet, or answering a direct question. But single-step agents are brittle. They assume everything is known up front. They can’t handle surprises or partial failures. You’ll quickly outgrow them when tasks involve multiple actions or require state tracking." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"If ethical lapses or AI failures occur, the impact on a business can be significant. Misinformation, biases, or harmful content generated by AI can lead to reputational damage, customer distrust, and potential regulatory scrutiny. The public relations fallout from an AI-driven error or ethical misstep can erode consumer confidence, resulting in lost revenue and lasting harm to brand image. Businesses, therefore, need to proactively address ethical considerations in AI implementation, not only to ensure compliance but also to protect and strengthen their reputation in a highly competitive, and increasingly transparent, marketplace." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 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)

"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)

"The promise of AI is its ability to process information objectively and at scale. However, this promise is fundamentally threatened by the twin challenges of bias and misinformation. AI systems are not born in a vacuum; they are created by humans and trained on data produced by humans. Consequently, they are prone to inheriting and even amplifying our prejudices, errors, and the systemic inequalities present in that data. In a RAG system, this risk is a two-fold problem: first in the retrieval of information, and second in the generation of a response based on that retrieval. A failure to address these issues doesn’t just lead to technically incorrect outputs; it can perpetuate social harm, erode public trust, and lead to the widespread dissemination of falsehoods. Therefore, understanding and mitigating bias and misinformation is not an optional add-on but a core requirement for any ethically deployed AI system." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

🤖Prompt Engineering: Learning (Just the Quotes)

"There is a plethora of credible scenarios for achieving human-level intelligence in a machine. We will be able to evolve and train a system combining massively parallel neural nets with other paradigms to understand language and model knowledge, including the ability to read and understand written documents. Although the ability of today's computers to extract and learn knowledge from natural-language documents is quite limited, their abilities in this domain are improving rapidly. Computers will be able to read on their own, understanding and modeling what they have read, by the second decade of the twenty-first century. We can then have our computers read all of the world's literature books, magazines, scientific journals, and other available material. Ultimately, the machines will gather knowledge on their own by venturing into the physical world, drawing from the full spectrum of media and information services, and sharing knowledge with each other (which machines can do far more easily than their human creators)." (Ray Kurzweil, "The Age of Spiritual Machines: When Computers Exceed Human Intelligence", 1999)

"The no free lunch theorem for machine learning states that, averaged over all possible data generating distributions, every classification algorithm has the same error rate when classifying previously unobserved points. In other words, in some sense, no machine learning algorithm is universally any better than any other. The most sophisticated algorithm we can conceive of has the same average performance (over all possible tasks) as merely predicting that every point belongs to the same class. [...] the goal of machine learning research is not to seek a universal learning algorithm or the absolute best learning algorithm. Instead, our goal is to understand what kinds of distributions are relevant to the 'real world' that an AI agent experiences, and what kinds of machine learning algorithms perform well on data drawn from the kinds of data generating distributions we care about." (Ian Goodfellow et al, "Deep Learning", 2015)

"Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations.  End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks. To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution." (Ashish Vaswani et al, "Attention Is All You Need", 2017)

"[...] building an effective LLM-based application can require more than just plugging in a pre-trained model and retrieving results - what if we want to parse them for a better user experience? We might also want to lean on the learnings of massively large language models to help complete the loop and create a useful end-to-end LLM-based application. This is where prompt engineering comes into the picture." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024) 

"Language modeling is a subfield of NLP that involves the creation of statistical/deep learning models for predicting the likelihood of a sequence of tokens in a specified vocabulary (a limited and known set of tokens). There are generally two kinds of language modeling tasks out there: autoencoding tasks and autoregressive tasks." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"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)

"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 with many other deep learning-based approaches, another major challenge is in interpretability. While knowledge graphs provide a structured and transparent way to store relationships, LLMs operate as a black box, making it difficult to understand how specific outputs are generated. [...] Data alignment is also a key issue, as structured knowledge graphs and unstructured text data must be carefully preprocessed to ensure consistency.  Differences in data formats, ontology mismatches, and information redundancy can create inefficiencies when integrating these two paradigms. Developing robust pipelines that seamlessly connect graph-based insights with LLM-generated text remains an open challenge." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Generative AI for coding and language tools is based on the LLM concept. A large language model is a type of neural network that processes and generates text in a humanlike way. It does this by being trained on a massive dataset of text, which allows it to learn human language patterns, as described previously. It lets LLMs translate, write, and answer questions with text. LLMs can contain natural language, source code, and  more." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"LLMs excel at understanding context and making associations among words, phrases, and concepts to provide relevant information based on the input query or prompt. While structured knowledge bases rely on humancurated data, LLMs can  automatically extract knowledge from unstructured text. When trained on diverse textual sources, they can process a vast amount of information without explicit human intervention. However, this also introduces a challenge, as the model can learn biased or incorrect information from the training data." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Transformers are complex models built like LEGO blocks using multiple smart and specialized components. [...] Briefly, a vanilla transformer model consists of separate stacks of encoders and decoders. Each encoder block includes multi-head self-attention, enabling the model to capture relationships between tokens regardless of their positions. Residual connections help maintain gradient flow, preventing the vanishing gradient problem. Layer normalization ensures training stability, and feed-forward layers introduce non-linearity and learn complex token interactions. Decoder blocks contain the same components but also include an encoder-decoder attention mechanism to incorporate context from the encoder. The model uses embedding layers to convert tokens into a continuous latent space for contextual learning and positional encoding to preserve the order of tokens in the sequence." (Joseph Babcock & Raghav Bali, "Generative AI with Python and PyTorch" 2nd. Ed., 2025)

"With MCP, a model no longer has to guess what’s possible. Instead, it can discover tools, query data sources, and select prompts - all in real time, all through a shared protocol. This means a model doesn’t just generate responses; it acts, it calls tools, it gathers context, and it learns how to interact with the outside world in a modular, controlled way." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Generative AI has a lot of problems because it needs a lot of data to work. Generative models like GPT and GANs need a lot of training data to learn patterns and make good outputs. This data dependency can cause problems like overfitting, which happens when the model does well on training data but doesn’t work well with new, unseen data. Also, the quality of the content that is generated is directly related to the diversity and representativeness of the training data. This means that biased or incomplete datasets can lead to outputs that are wrong or unfair." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"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

"Intelligent systems connect users to AI and ML to achieve meaningful objectives. An intelligent system is one in which intelligence evolves and improves over time, particularly when it improves by watching how users interact with the system.[...] The primary objective of the intelligent system is to support users in accomplishing complex tasks - not by replacing them, but by enhancing their decision-making capabilities. [...] An intelligent system must also have the ability to learn from user interactions and explicit feedback, as well as utilize contextual information. The system should contin-uously develop, use, and maintain an evolving knowledge base. This evolution is driven not only by data sources but also by ongoing interactions with users." (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)

"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)

"Retrieval mechanisms are essential for addressing some of the key limitations of traditional generative AI models, such as factual inaccuracies, lack of context awareness, and model hallucinations. While generative models excel at creating coherent and fluent content, they often struggle to produce outputs that are factually correct or contextually relevant. This is because these models rely solely on patterns learned during training, without access to real-time or external information. For example, a generative model might generate a plausible-sounding but incorrect answer to a factual question, as it cannot verify the accuracy of its response." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

🤖〽️Prompt Engineering: Copilot Unabridged (Part 124: The Impact of Algorithmic Bias on AI: Why It Matters More Than Ever)

Prompt Engineering Series

Prompt: "write a blog post of 600 words on the impact of algorithmic bias on AI"

Introduction

Artificial intelligence (AI) has become woven into the fabric of modern life. It recommends what we watch, filters what we read, evaluates our job applications, approves our loans, and increasingly shapes decisions once made exclusively by humans. Yet beneath the surface of this technological progress lies a persistent and often invisible challenge: algorithmic bias. As AI systems grow more influential, understanding the origins, consequences, and solutions to bias becomes essential - not just for technologists, but for anyone living in a world mediated by algorithms.

What Is Algorithmic Bias?

Algorithmic bias occurs when an AI system produces systematically unfair outcomes - favoring or disadvantaging certain groups - because of patterns embedded in its data, design, or deployment. Contrary to popular belief, bias in AI is rarely the result of malicious intent. Instead, it emerges from the simple fact that AI learns from historical data, and history is full of human imperfections.

If the data reflects societal inequalities, the model will learn those inequalities. If the training set underrepresents certain populations, the model will perform worse for them. And if the objectives or constraints are poorly defined, the system may optimize for the wrong outcomes entirely.

In other words, AI doesn’t just mirror the world - it can magnify its flaws.

Where Bias Creeps In

Bias can enter an AI system at multiple stages:

1. Biased Training Data

AI models learn statistical patterns from examples. If those examples are skewed, incomplete, or unrepresentative, the model inherits those distortions. Classic cases include facial recognition systems that perform poorly on darker skin tones because the training data was overwhelmingly composed of lighter-skinned faces.

2. Problem Framing and Design Choices

Even before data enters the picture, human decisions shape the system. What is the model optimizing for? What counts as a 'successful' prediction? Which variables are included or excluded? These choices embed assumptions that can unintentionally privilege certain outcomes.

3. Feedback Loops in Deployment

Once deployed, AI systems can reinforce their own biases. For example, predictive policing tools may direct more patrols to neighborhoods flagged as 'high risk', generating more recorded incidents and further validating the model’s initial assumptions - even if the underlying crime rates were similar elsewhere.

Why Algorithmic Bias Matters

The consequences of biased AI are not abstract - they affect real people in tangible ways.

1. Inequitable Access and Opportunity

Biased hiring algorithms may filter out qualified candidates. Loan approval models may disproportionately reject applicants from certain demographics. Healthcare algorithms may underestimate risk for specific populations, leading to worse outcomes.

2. Erosion of Trust

When people experience or perceive unfairness in algorithmic decisions, trust in institutions erodes. This is especially damaging in sectors like government, healthcare, and finance, where legitimacy depends on perceived fairness.

3. Amplification of Social Inequalities

AI systems operate at scale. A biased decision made once by a human affects one person; a biased decision made by an algorithm affects thousands or millions. Without intervention, AI can accelerate and entrench existing disparities.

Toward Fairer, More Responsible AI

Addressing algorithmic bias is not a one-time fix - it’s an ongoing process requiring technical, organizational, and societal commitment.

1. Better Data Practices

Diverse, representative datasets reduce the risk of skewed outcomes. Equally important is transparency about data sources and limitations.

2. Rigorous Testing and Auditing

Bias detection tools, fairness metrics, and independent audits help identify issues before they cause harm. Continuous monitoring ensures that systems remain fair as conditions change.

3. Human Oversight and Accountability

AI should augment - not replace - human judgment. Clear accountability structures ensure that organizations remain responsible for the outcomes of their systems.

4. Ethical and Regulatory Frameworks

Governments and industry bodies are increasingly developing guidelines to ensure fairness, transparency, and accountability. These frameworks help align AI development with societal values.

Conclusion

Algorithmic bias is not an inevitable flaw of AI - it’s a challenge we can address with thoughtful design, responsible data practices, and ongoing oversight. As AI continues to shape our world, the choices we make today will determine whether these systems reinforce old inequities or help build a more just and inclusive future.

Disclaimer: The whole text was generated by Copilot (under Windows 10) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖Prompt Engineering: Errors (Just the Quotes)

"The no free lunch theorem for machine learning states that, averaged over all possible data generating distributions, every classification algorithm has the same error rate when classifying previously unobserved points. In other words, in some sense, no machine learning algorithm is universally any better than any other. The most sophisticated algorithm we can conceive of has the same average performance (over all possible tasks) as merely predicting that every point belongs to the same class. [...] the goal of machine learning research is not to seek a universal learning algorithm or the absolute best learning algorithm. Instead, our goal is to understand what kinds of distributions are relevant to the 'real world' that an AI agent experiences, and what kinds of machine learning algorithms perform well on data drawn from the kinds of data generating distributions we care about." (Ian Goodfellow et al, "Deep Learning", 2015)

"The art of mega-prompts spanning multiple written pages and looking like essays has become commonplace for complex tasks when building applications to get things `just right'. Unfortunately, they bring with them lots of issues: errors, portability, complexity, and more. The GenAI world didn’t plan for mega-prompts. They have simply evolved into what they’ve become today because practitioners kept wanting to do more and more complex things, and their only way to express those intents was with a prompt. But step back and look at some of these prompts [...] Lurking just below the surface are a bunch of classical computing concepts like data, programming instructions, control flows, memory, and stora - all the components typically associated with classical computing elements." (Rob Thomas et al, "AI Value Creators: Beyond the Generative AI User Mindset", 2025)

"The same difficulties that characterize training deep feedforward networks also apply to RNNs; gradients tend to die out over long distances using traditional activation functions (or explode if the gradients become greater than 1). However, unlike feedforward networks, RNNs aren’t trained with traditional backpropagation, but rather a variant known as Backpropagation through Time (BPTT): the network is unrolled, as before, and backpropagation is used, averaging over errors at each time point (since an 'output', the hidden state, occurs at each step). Also, in the case of RNNs, we run into the problem that the network has a very short memory; it only incorporates information from the most recent unit before the current one and has trouble maintaining long-range context. For applications such as translation, this is clearly a problem, as the interpretation of a word at the end of a sentence may depend on terms near the beginning, not just those directly preceding it." (Joseph Babcock & Raghav Bali, "Generative AI with Python and PyTorch" 2nd. Ed., 2025)

"If ethical lapses or AI failures occur, the impact on a business can be significant. Misinformation, biases, or harmful content generated by AI can lead to reputational damage, customer distrust, and potential regulatory scrutiny. The public relations fallout from an AI-driven error or ethical misstep can erode consumer confidence, resulting in lost revenue and lasting harm to brand image. Businesses, therefore, need to proactively address ethical considerations in AI implementation, not only to ensure compliance but also to protect and strengthen their reputation in a highly competitive, and increasingly transparent, marketplace." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 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 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)

"The promise of AI is its ability to process information objectively and at scale. However, this promise is fundamentally threatened by the twin challenges of bias and misinformation. AI systems are not born in a vacuum; they are created by humans and trained on data produced by humans. Consequently, they are prone to inheriting and even amplifying our prejudices, errors, and the systemic inequalities present in that data. In a RAG system, this risk is a two-fold problem: first in the retrieval of information, and second in the generation of a response based on that retrieval. A failure to address these issues doesn’t just lead to technically incorrect outputs; it can perpetuate social harm, erode public trust, and lead to the widespread dissemination of falsehoods. Therefore, understanding and mitigating bias and misinformation is not an optional add-on but a core requirement for any ethically deployed AI system." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)

"With autonomous agents there is the risk that they can take action that isn’t governed. These systems introduce autonomy, interdependence between agents, and possibly emergent behavior. [...] Because agents act autonomously, a single misconfigured or misaligned agent can propagate errors at scale. In multiagent environments, one faulty output can trigger many downstream mistakes. Other risks include tool misuse, conflicting goals among agents (and other interoperability issues), and operational opacity, when teams cannot easily determine which agent took which action or why. Governance must extend to agent registration, version control, permissioning, and simulation testing before deployment. Likewise, accountability may also blur. If an agent takes a dangerous action, who is responsible? There are, of course, cybersecurity risks as agents pose a new attack surface."  (Fern Halper, "Data Makes the World Go 'Round", 2026)

🤖Prompt Engineering: Models (Just the Quotes)

"An internal model allows a system to look ahead to the future consequences of current actions, without actually committing itself to those actions. In particular, the system can avoid acts that would set it irretrievably down some road to future disaster ('stepping off a cliff'). Less dramatically, but equally important, the model enables the agent to make current 'stage-setting' moves that set up later moves that are obviously advantageous. The very essence of a competitive advantage, whether it be in chess or economics, is the discovery and execution of stage-setting moves." (John H Holland, 1992)

"[...] building an effective LLM-based application can require more than just plugging in a pre-trained model and retrieving results - what if we want to parse them for a better user experience? We might also want to lean on the learnings of massively large language models to help complete the loop and create a useful end-to-end LLM-based application. This is where prompt engineering comes into the picture." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"Agentic workflows break when the logic is messy - if, say, the plans don’t decompose or memory is poorly structured. However, infrastructure-level LLM applications introduce even more failure points and complexity. If the protocols don’t sync with each other, or the data flows start leaking, or the model boundaries are unclear... there are far too many failure points to count. While most people have been jumping on the bandwagon to adopt MCPs or A2A, very few are equipped to handle the LLMOps issues these tools introduce." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"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)

"In prompt engineering, we customize the prompts or questions we give the model to get more accurate or insightful responses. The way a prompt is structured has a massive impact on how well a model understands the task at hand and, ultimately, how well it performs. Given LLMs’ versatility, prompt engineering has become an important skill for getting the most out of these models across different domains and tasks. The key is to understand how different prompt structures lead to different model behaviors. There are various strategies - ranging from simple one-shot prompting to more complex techniques like chain-of-thought prompting - that can significantly improve the effectiveness of LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"[...] prompt engineering, the science and art of crafting the text inputs that are sent to the models. Prompt updates can significantly improve or degrade the user experience. But prompt engineering is iterative and can be difficult to master and document, especially with closed-source LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Prompt engineering is a crucial aspect of working with large language models (LLMs) like OpenAI's GPT, Google's PaLM, and others in the space of AI and machine learning. It involves the art and science of designing inputs (prompts) in a way that maximizes the quality, relevance, and accuracy of the AI-generated output. As the capabilities of AI continue to improve, the task of crafting effective prompts has become an essential skill for anyone leveraging these tools for real-world applications, including natural language understanding, translation, summarization, code generation, and more." (Code Planet, "Python for Large Language Models", 2025)

"Prompt injection is a security vulnerability that is specific to AI systems, especially LLM systems, in which malicious users try to manipulate prompts to make a model behave in a certain unintended way. They may try to get it to leak data, execute unauthorized tasks (especially with agentic systems), or ignore constraints. This is possible because LLMs are typically encapsulated inside applications using metaprompts, which are developer-created instructions that define the model’s behavior. Metaprompts usually contain safeguard instructions, such as 'do not use curse words', and placeholders where the input submitted by the user is pasted. The user’s input is combined with the metaprompts into a larger prompt that then goes to the model." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 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)

"With MCP, a model no longer has to guess what’s possible. Instead, it can discover tools, query data sources, and select prompts - all in real time, all through a shared protocol. This means a model doesn’t just generate responses; it acts, it calls tools, it gathers context, and it learns how to interact with the outside world in a modular, controlled way." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

🤖〽️Prompt Engineering: Copilot Unabridged (Part 123: Why AI Is Not Bulletproof in Automation Scenarios)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on why AI is not bulletproof is automation scenarios"

Introduction

Artificial Intelligence (AI) has become the poster child of modern automation. From supply‑chain optimization to customer service chatbots to predictive maintenance in factories, it’s easy to assume that AI systems are precise, tireless, and - at least in theory - nearly infallible. But that assumption is exactly where organizations get into trouble. AI is powerful, yes, but bulletproof? Not even close. And understanding why it isn’t bulletproof is essential for anyone deploying automation in the next decade.

Let’s unpack the cracks beneath the shiny surface.

AI Learns From Data - And Data Is Messy

AI systems don’t understand the world; they understand patterns in data. And real‑world data is full of noise, bias, gaps, and contradictions.

• A model trained on historical hiring data may inherit past discrimination.
• A predictive maintenance system may fail if sensors degrade or environmental conditions shift.
• A customer‑service bot may misinterpret a request simply because the phrasing wasn’t in its training set. 

When the data is imperfect, the automation built on top of it inherits those imperfections. AI doesn’t magically 'fix' flawed data - it amplifies it.

Automation Assumes Stability, but the Real World Is Dynamic

Traditional automation works best in stable, predictable environments. AI‑driven automation is more flexible, but it still struggles when the world changes faster than the model can adapt.

Consider:

• Sudden market shifts
• New regulations
• Unexpected supply‑chain disruptions
• Novel user behaviors
• Rare edge‑case events

AI models trained on yesterday’s patterns can’t automatically understand tomorrow’s anomalies. Without continuous monitoring and retraining, automation becomes brittle.

AI Doesn’t 'Understand' - It Correlates

Even the most advanced AI systems don’t possess human‑level reasoning or contextual awareness. They operate on statistical correlations, not comprehension.

This leads to automation failures like:

• Misclassifying harmless anomalies as threats
• Failing to detect subtle but critical changes
• Producing confident but incorrect outputs
• Following rules literally when nuance is required

In high‑stakes environments - healthcare, finance, transportation - this lack of true understanding becomes a serious limitation.

Edge Cases Are the Achilles’ Heel

AI performs impressively on common scenarios but struggles with rare events. Unfortunately, automation systems often encounter exactly those rare events.

Examples include:

• A self‑driving car encountering an unusual road layout
• A fraud‑detection model missing a novel attack pattern
• A warehouse robot misinterpreting an unexpected obstacle

Humans excel at improvisation; AI does not. Automation breaks down when reality refuses to fit the training distribution.

Security Vulnerabilities Undermine Reliability

AI systems introduce new attack surfaces:

• Adversarial inputs can trick models with tiny, invisible perturbations.
• Data poisoning can corrupt training sets.
• Model inversion can leak sensitive information.
• Prompt manipulation can cause unintended behavior in language models.
• Automation built on AI can be manipulated in ways traditional systems never could.

Ethical and Governance Gaps Create Operational Risk

Even when AI works “correctly,” it may still cause harm if governance is weak.

Automation can:

• Reinforce bias
• Reduce transparency
• Remove human oversight
• Make decisions that lack accountability

Organizations often underestimate the social and regulatory risks of automated decision‑making. AI isn’t just a technical system - it’s a socio‑technical one.

Humans Are Still Part of the System

The biggest misconception about automation is that it removes humans. In reality, it changes the role of humans.

People must:

• Monitor AI outputs
• Intervene during failures
• Interpret ambiguous results
• Maintain and retrain models
• Handle exceptions and edge cases

If humans aren’t properly trained or workflows aren’t redesigned, automation becomes fragile.

The Bottom Line: AI Is Powerful, but Not Invincible

AI can supercharge automation, but it’s not a magic wand. It’s a tool - one that requires careful design, continuous oversight, and a deep understanding of its limitations. Organizations that treat AI as bulletproof will face costly failures. Those that treat it as a dynamic, fallible component of a broader ecosystem will unlock its real value.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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