Hands On Large Language Models

The distinguishing feature of this book is not its topic list — RAG, fine-tuning, embeddings and chatbots are covered everywhere by now — but the density of its visual explanations. More than 275 figures are custom-drawn rather than lifted from papers, and a running timeline of LLM development gives you historical context that most tutorials skip. The labs are concrete: semantic search built on sentence-transformers, a retrieval-augmented generation pipeline over your own knowledge base, fine-tuning a small open model for a domain task, and a chatbot that maintains context. Because the repo ships interactive Jupyter notebooks, you can modify the examples rather than retype them. The author pairing is a genuine signal — Jay Alammar's AI blog is one of the most widely read visual explainers in machine learning, and Maarten Grootendorst maintains BERTopic, KeyBERT and PolyFuzz, so the representational side of the book is written by someone who ships embedding-based tooling. Weaknesses are real and worth stating. It is a 2024 print and ebook product, not a living platform, so nothing in it tracks the model releases, API pricing or interface changes of the last two years; you will need the vendors' own current docs for anything version-bound. It covers concepts rather than a single proprietary model, which is a strength for durability and a weakness if you wanted a guided tour of one specific API. And the code assumes you are already comfortable in Python and Jupyter — this is not a first programming book. Where it fits: a developer or data scientist who has used an LLM API and now wants to understand what is happening underneath and how to build search and RAG on top. Where it does not: researchers wanting unpublished techniques, and anyone who prefers video courses.