Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 11 — Attack Surfaces and Protocol Vulnerabilities
Eleventh post of the LLM Primer IV walkthrough. The classical attacks adapted to MCP — Confused Deputy, Token Passthrough, Session Hijacking — the protocol-level flaws around capability escalation and unauthenticated sampling, and the implicit trust propagation that makes context poisoning a structural problem rather than a hygiene one.
2026-04-09Chapter 9 — Managing the Attention Budget
Ninth post of the LLM Primer IV walkthrough. Context rot, the lost-in-the-middle cliff, tool-loadout rot, and the three architectural answers — MCP, RAG, fine-tuning — to the question of where a model's missing knowledge actually belongs.
2026-04-07Chapter 8 — Architectural Deployment Layouts
Eighth post of the LLM Primer IV walkthrough. The three deployment layouts that have emerged in the MCP ecosystem — reusable agent, strict purity, hybrid — and the four binding constraints that determine which one fits which project.
2026-04-06Chapter 6 — Fundamental Orchestration Strategies
Sixth post of the LLM Primer IV walkthrough. The two foundational orchestration shapes — sequential pipelines and concurrent scatter-gather — and the prior question every team should ask: is a multi-agent system the right answer at all?
2026-04-04Chapter 5 — Transport Protocols and Discovery
Fifth post of the LLM Primer IV walkthrough. The three transports MCP supports, the .well-known discovery layer with Server Cards, and the boring operational concerns — CORS, origin validation, caching — that decide whether a server is a cooperative network citizen or a liability.
2026-04-03Chapter 4 — Client Primitives: Agentic Behaviors and Control
Fourth post of the LLM Primer IV walkthrough. Sampling, Roots, and Elicitation are the three small, controlled holes MCP punches through the host-server wall — each a capability granted back, each a risk accepted on the user's behalf.
2026-04-02
Chapter 11 — Continuous Updates and Pipeline Optimization
Eleventh and final post of the LLM Primer III walkthrough. CDC and incremental indexing keep the corpus fresh, semantic caching and model tiering keep latency down, and a four-stage feedback loop closes the gap between what production tells the team and what the team actually changes — plus a bridge to Volume IV on Model Context Protocol.
2026-03-28
Chapter 10 — Leading Evaluation Frameworks
Tenth post of the LLM Primer III walkthrough. A field guide to the frameworks that turn the Evaluation Triad into something a team can actually run — RAGAS, TruLens, DeepEval on one side, Braintrust, LangSmith, Phoenix, Galileo, Opik on the other, and the Evaluation Gap none of them has yet closed.
2026-03-27
Chapter 9 — The RAG Evaluation Triad
Ninth post of the LLM Primer III walkthrough. A RAG system can fail in three different places and the failures look identical from the outside — the Evaluation Triad of Context Relevance, Groundedness, and Answer Relevance is the small vocabulary that prevents fixing one bug while measuring another.
2026-03-26
Chapter 8 — Data Anonymization in the RAG Pipeline
Eighth post of the LLM Primer III walkthrough. Pre-generation versus post-generation anonymisation, the three technique families — masking, synthetic replacement, differential privacy — and the utility-privacy tradeoff that determines whether the system remains useful at all.
2026-03-25
Chapter 7 — Implementing Access Control
Seventh post of the LLM Primer III walkthrough. Document-level ACLs as the foundation, RBAC with Microsoft Purview sensitivity labels, ReBAC with Zanzibar and SpiceDB, and the pre-filter versus post-filter discipline that runs underneath all of them.
2026-03-24
Chapter 6 — RAG Threat Models and Vulnerabilities
Sixth post of the LLM Primer III walkthrough. The expanded attack surface of retrieval — corpus poisoning, adversarial chunks, indirect prompt injection, embedding inversion, and the confused-deputy problem in agentic RAG. Concrete attacks, each demonstrated, each reproducible.
2026-03-23
Chapter 5 — Architecting the Retrieval Pipeline
Fifth post of the LLM Primer III walkthrough. Why a single vector search is not a pipeline — hybrid retrieval, reciprocal rank fusion, cross-encoder reranking, and query-side rewriting and HyDE — assembled into the production architecture that mature RAG systems converge on.
2026-03-22
Chapter 3 — Advanced Chunking Frameworks
Third post of the LLM Primer III walkthrough. The chunking spectrum from fixed-size to structure-aware, the overlap myth, the context cliff that destroys retrieval quietly, and the contextual-retrieval and late-chunking techniques that have reshaped the frontier.
2026-03-20
Chapter 2 — Intelligent Document Parsing
Second post of the LLM Primer III walkthrough. Why a PDF is not a text file, what layout-aware parsers actually preserve, the current tool landscape (LlamaParse, Docling, Unstructured, Marker-PDF, Firecrawl, DeepSeek-OCR), and the multimodal track that retrieves over page images directly.
2026-03-19
Chapter 1 — The Evolution of RAG Architecture
First post of the LLM Primer III walkthrough. The four architectural postures of RAG — Naive, Advanced, Modular, Agentic — read as a story about handing more agency to the LLM one decision at a time, and the honest answer to when fine-tuning is the better tool than retrieval.
2026-03-18
LLM Primer III — Series Introduction & Index
Kicking off the chapter-by-chapter walkthrough of Book III in the LLM Primer series — Enhancing Enterprise AI with RAG. Why retrieval-augmented generation looks simple from the outside and is a stack of disciplines underneath, who this book is for, and the schedule for the eleven posts that follow, March 18 through March 28.
2026-03-17
Chapter 12 — Real-World Applications of LLMs
Twelfth post of the LLM Primer II walkthrough. Text generation, summarization, QA, translation, reasoning — and the constrained decoding, agent loops, and multimodal generalization that turn one next-token machine into a dozen kinds of product.
2026-03-14
Chapter 8 — Using LLMs in Applications: Chatbots, Code, Extraction, and Agents
Chapter 8 of the LLM Primer I series. The application patterns that actually ship in production — chatbots, summarization, code assistants, structured extraction, and the rise of agentic systems where the model drives a tool-use loop. Plus the benchmarks every engineer should recognize by name.
2026-02-25
Chapter 7 — Beyond Next-Token Prediction: Embeddings, Retrieval, and Multimodality
Chapter 7 of the LLM Primer I series. The capabilities that turn a next-token predictor into something much more — embeddings, semantic search, retrieval-augmented generation, and the move into multimodal inputs. How RAG actually keeps an LLM grounded in real documents instead of confabulating.
2026-02-24
5.1 Bias & Ethical Considerations
A preview from Chapter 5.1 of our book: uncover how large language models inherit bias and learn strategies to build fair, trustworthy AI.
2024-09-29
4.3 LLMs in Translation and Summarization: Enhancing Multilingual Communication
Learn how Large Language Models (LLMs) leverage Transformer architectures for accurate translation and summarization, improving efficiency in business, media, and education.
2024-09-18
3.1 LLM Training: Dataset Selection and Preprocessing Techniques
Learn about dataset selection and preprocessing techniques for training Large Language Models (LLMs). Explore steps like noise removal, tokenization, normalization, and data balancing for optimized model performance.
2024-09-12
2.3 Key LLM Models: BERT, GPT, and T5 Explained
Discover the main differences between BERT, GPT, and T5 in the realm of Large Language Models (LLMs). Learn about their unique features, applications, and how they contribute to various NLP tasks.
2024-09-10
1.2 The Role of Large Language Models (LLMs) in Natural Language Processing (NLP)
Discover the impact of Large Language Models (LLMs) on natural language processing tasks. Learn how LLMs excel in text generation, question answering, translation, summarization, and even code generation.
2024-09-04