Introduction

This book combines two formerly separate sets of study notes: foundations of generative AI and large language models, and practical workflows for working with Claude and AI coding agents. The goal is to connect model concepts with safe, reproducible practice for data science and software work.

The book has three parts:

  1. LLM foundations — transformers and attention, pre-training and scaling laws, fine-tuning and PEFT, evaluation, alignment (RLHF, DPO), and building LLM applications (inference optimization, RAG, tools, responsible AI).
  2. Prompt engineering — prompt anatomy, techniques, use-case strategies, evaluation, and safety (in progress).
  3. Applied agent workflows — prompting, code review, verification loops, Claude Code hooks and skills, and reproducible notebooks.

An appendix summarizes the hands-on labs that accompanied the LLM foundations material.

These are educational notes, not vendor certification material. Product features change quickly, so product-specific chapters link to primary vendor documentation and should be checked against the installed product version.

How to use this book

  • Read the LLM chapters in sequence when learning the underlying concepts.
  • Use the Claude chapters as practical references for agent-assisted work.
  • Run and inspect generated code; do not treat model output as validated merely because it is fluent.
  • Protect secrets and private data, use least-privilege tool permissions, and keep a human reviewer responsible for consequential decisions.