Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
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 14 — Practical Knowledge for Engineers
Twelfth post — the closing chapter of the LLM Primer II walkthrough. How to keep deepening your understanding after the book ends, the tools and libraries that turn the math into shipping work, and the bridge to the other books in the LLM Primer series.
2026-03-16
Chapter 3 — Mathematical Tools for Language Models
Third post of the LLM Primer II walkthrough. The probability and statistics you actually need for language modeling, the slice of linear algebra that matters, and embeddings as the first place those two tools meet inside an LLM.
2026-03-05
6.1 Introducing Open-Source Tools and APIs
A preview from Chapter 6.1: Explore Hugging Face, OpenAI, Google Cloud Vertex AI, and Azure Cognitive Services—leading tools to bring LLMs into your projects.
2024-10-04
6.0 Hands-On with LLMs
A preview from Chapter 6: Learn how to run large language models yourself with open-source libraries, cloud APIs, and Python—making LLMs accessible to everyone.
2024-10-02