Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 16 — Cost-Cutting Strategies in Production
Sixteenth and final post of the LLM Primer VI walkthrough. Intelligent model routing, context compaction, async batch APIs, and semantic caching — plus a look ahead to Volume VII on AI Security.
2026-05-08
Chapter 15 — Serverless APIs vs Dedicated Infrastructure
Fifteenth post of the LLM Primer VI walkthrough. The breakeven math between serverless APIs and dedicated infrastructure, the hidden platform-engineering overhead each side takes on, and microVM sandboxes for agent code execution.
2026-05-07
Chapter 3 — Retrieval-Augmented Generation
Third post of the LLM Primer V walkthrough. The RAG pipeline end to end — chunking, hybrid retrieval, query transformation, multimodal, and text-to-SQL — and where RAG fits versus fine-tuning and long context.
2026-04-16
Chapter 2 — Foundation Models & Prompt Engineering
Second post of the LLM Primer V walkthrough. Model tiering, sampling parameters, defensive prompt patterns, and structured outputs as engineering surfaces — the layer just inside the deterministic wrapper.
2026-04-15
Chapter 1 — The Discipline of AI Engineering
First post of the LLM Primer V walkthrough. Why the demo works and production doesn't — the deterministic wrapper around the probabilistic core, and the five pillars (reliability, quality, performance, cost, evolution) that keep the wrapper honest.
2026-04-14
LLM Primer V — Series Introduction & Index
Kicking off the chapter-by-chapter walkthrough of Book V in the LLM Primer series — Building Real-World LLM Applications. Why AI engineering is a discipline of its own, who this book is for, and the schedule for the eight posts that follow, April 14 through April 21.
2026-04-13
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.
2026-04-09
Chapter 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.
2026-04-07
Chapter 3 — Server Primitives: Exposing Context and Capabilities
Third post of the LLM Primer IV walkthrough. The three nouns an MCP server can offer — Resources (read state), Prompts (reusable scaffolding), Tools (write actions) — their schemas, their lifecycles, their error models, and the discipline of choosing the right primitive.
2026-04-01
Chapter 2 — Unveiling the Model Context Protocol (MCP)
Second post of the LLM Primer IV walkthrough. What MCP actually standardizes, the three-role split of Host, Client, and Server, why dynamic discovery and bidirectional messaging differ from REST in the cases that matter, and the session lifecycle that opens with capability negotiation.
2026-03-31
Chapter 1 — The AI Integration Crisis and the Rise of Agentic Architecture
First post of the LLM Primer IV walkthrough. Why monolithic agents fray as system prompts grow, the N times M integration problem hiding underneath, and the move from prompt engineering to context engineering that MCP was built to enable.
2026-03-30
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 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 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 — LLMs in Context: Concepts and Background
Second post of the LLM Primer II walkthrough. What an LLM actually is, the three things "pretraining, parameters, scale" really stand for, the unusual nature of language as a data source, and why the transformer rewrote the field in a single year.
2026-03-04
Chapter 9 — Performance, Scaling, and Costs: The Real Engineering Trade-offs
Chapter 9 of the LLM Primer I series. The operational realities of running LLMs at scale — model size vs capability, the latency–throughput trade-off, cost economics, quantization, and edge deployment. Why frontier-tier models are often the wrong choice even when you can afford them.
2026-02-26
Chapter 5 — Training Large Models: What Actually Goes Into a Frontier Model
Chapter 5 of the LLM Primer I series. How frontier LLMs are actually trained — the data pipeline, the loss function, the months of GPU time, and why "training" is now an industrial-scale engineering problem more than a research problem. Demystifies what those hundred-million-dollar training runs are paying for.
2026-02-22Chapter 2 — LLMs in Context: Concepts and Background
An accessible introduction to Chapter 2 of Understanding LLMs Through Math. Explore what Large Language Models are, why pretraining and parameters matter, how scaling laws shape model performance, and why Transformers revolutionized NLP. This chapter provides essential context before diving deeper into the mechanics of modern LLMs.
2025-09-07