Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 4 — AI Agents and Tool Calling
Fourth post of the LLM Primer V walkthrough. ReAct loops, tool schemas as contracts, and the three memory layers agents actually need in production.
2026-04-17
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
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 10 — Long-Horizon Task Memory
Tenth post of the LLM Primer IV walkthrough. Short-term memory through windows and ReAct scratchpads, long-term memory through episodic vectors and semantic stores, and the compaction techniques that keep an agent productive over hours and days.
2026-04-08
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
4.4 How LLMs Write Code: The Rise of AI-Powered Programming Assistants
Explore how large language models (LLMs) generate and complete code from natural-language prompts, and what it means for the future of software development.
2024-09-27