"LLMs are trained on large volumes of data, which inherently provides them with an immense knowledge base and understanding of different languages. Yet, LLMs at their core are complex text completion engines. Since this knowledge and understanding of language is compressed in a very high-dimensional latent space. LLMs end up using these in a very fluid and intelligible way (which often leads to hallucinations). In order to guide LLMs to focus on specific topics or pieces of information to solve certain tasks, (for instance, question-answering from a given piece of text), it is important to provide contextual information explicitly. While most current generations of LLMs have extremely wide context windows, it is recommended to preprocess context into overlapping smaller chunks for better results, reduced latency, and so on. For similar reasons, it is also recommended to preprocess contextual information in clear and task-specific formats. This aspect of context preprocessing is extremely useful in Retrieval-Gugmented Generation (RAG) scenarios." (Joseph Babcock & Raghav Bali, "Generative AI with Python and PyTorch" 2nd. Ed., 2025)
"Semantic Kernel is a framework designed to simplify integrating LLMs into applications that require dynamic knowledge, reasoning, and state tracking. It’s particularly useful when you want to build complex, modular AI systems that can interact with external APIs, knowledge bases, or decision-making processes. Semantic Kernel focuses on building more flexible AI systems that can handle a variety of tasks beyond just generating text. It allows for modularity, enabling developers to easily combine different components - such as embeddings, prompt templates, and custom functions - in a cohesive manner." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)
"Agentic AI extends GenAI by embedding intelligence within autonomous or semi-autonomous systems that can plan, reason, and take actions within defined boundaries. Instead of simply generating a report, an agentic system might determine which data it needs, retrieve that data from multiple sources, perform analysis, summarize the results, and then trigger follow-up workflows, all while maintaining auditability and alignment with governance policies." (Fern Halper, "Data Makes the World Go 'Round", 2026)
"Human-in-the-loop (HITL) is a paradigm that formally integrates human expertise into the AI workflow, creating a collaborative partnership between human and machine intelligence. This approach is essential for high-consequence applications where full automation is too risky, such as medical diagnosis, legal contract review, or content moderation. In a RAG system, the human acts as a validator, auditor, and final decision-maker. The AI handles the heavy lifting of information retrieval and draft generation, while the human provides the critical judgment, context, and ethical reasoning that the AI lacks." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)
"[...] KGs and LLMs can be the foundation for different types of reasoning, complementing each other in intelligent systems. We can use KGs for tasks that require precise, rule-based reasoning and explicit knowledge representation, and LLMs for tasks involving pattern recognition, context understanding, handling ambiguity or incomplete information, and reasoning about graph structures and their derived metrics. However, neither approach inherently possesses common-sense reasoning capabilities comparable to those of humans, and they often fail to make intuitive leaps or understand the implicit context that would be obvious to a person. These limitations underscore the importance of carefully considering the strengths and weaknesses of each approach when designing intelligent systems and potentially developing a powerful hybrid IAS." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)
"Knowledge representation is the 'language' that AI systems use to structure, encode, and communicate information about the domain they are modelling. AI systems need it to reason or predict, and we use it to interpret the results they produce. The choice of how to represent knowledge can significantly affect the capabilities and limitations of the AI system." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)
"LangGraph handles the perception, reasoning, and action flow while maintaining memory and context across tasks. In this sense, it functions as both the development environment and the orchestration layer - coordinating the steps of perception, reasoning, and action while managing connections to external systems. Underneath this, emerging standards like MCP ensure that agents can connect securely and consistently to tools and data sources, making agentic architectures portable and scalable across platforms. Taken together, the orchestration layer and emerging interoperability standards like MCP form the foundation for scalable agentic AI. They make it possible for agents to perceive, reason, act, and learn in coordinated ways across complex environments, translating autonomous intelligence into practical, enterprise-grade capability." (Fern Halper, "Data Makes the World Go 'Round", 2026)
"[...] LLMs raise serious concerns about ethics, bias and fairness, errors in reasoning, hallucinations, and misuse (e.g., misinformation and disinformation). These concerns are exacerbated by modern LLMs being both literal and figurative 'black boxes': Literal black boxes because many advanced AI systems are proprietary and the weights (trained parameters of the models) are not released to the public; and figurative black boxes because even the open-source AI models are so complicated that understanding them and developing safety guardrails has thus far proven extremely difficult." (Mike X Cohen,"50 ML Projects To Understand LLMs", 2026)
"The Chain of Thought pattern uses chain-of-thought (CoT) reasoning, which is a prompting technique that enhances LLMs’ problem-solving capabilities by encouraging them to break down complex problems into intermediate reasoning steps before arriving at a final answer. [...] CoT involves modifying the prompting strategy - instead of asking for a direct answer, the prompt explicitly requests or demonstrates a step-by-step reasoning process. There are three variants: Zero-shot CoT, Few-shot CoT, and Auto-CoT. [...] In Zero-shot CoT, you request a step-by-step reasoning by simply adding the phrase think step-by-step to the prompt. [...] In cases where the model’s pretrained capabilities do not include the specific logic needed, you can try Few-shot CoT, which involves providing the model with examples of step-by-step reasoning for similar problems before asking it to solve a new problem. In Few-shot CoT, you demonstrate the desired reasoning pattern through examples. [...] In the Auto-CoT variant, you add demonstrative examples to a wide variety of questions to a database called an example store. Most people use a document database (in which the examples are indexed by keywords in the question text) or a vector database (in which the examples are indexed by the embedding of the questions) for the example store." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)
"The Reflection pattern is an agentic approach in which an AI system evaluates its own reasoning, decisions, and outputs before finalizing them. This self-monitoring process allows the system to identify errors, improve solutions, and refine its approach to problems. Here, the AI system as a whole is reflecting and improving, and the foundational model used to generate the response remains the same. You’ll sometimes see this process referred to as self-reflection, but there’s no reason why the evaluation has to be done by an LLM at all, let alone by the same LLM that generates the content." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)
