If you want to become an AI Engineer, start with these 10 books:

1) The LLM Engineering Handbook - buff.ly/wogklbo

The best book to understand the full LLM stack — embeddings, vector search, retrieval-augmented generation (RAG), and agent design patterns.

2) AI Engineering by Chip Huyen - buff.ly/Nf1RMHU

Probably the most recommended book for AI system builders — covers MLOps, real-time inference, and production-readiness.

3) Designing Machine Learning Systems - buff.ly/EfN5uOE

Before you train your first model, read this. It’ll show you how to build end-to-end ML pipelines that scale.

4) Building LLMs for Production - buff.ly/wjpOeTB

Shipping LLMs to production is hard. This book teaches you how to fine-tune, scale, and deploy them reliably.

5) Build a Large Language Model (from Scratch) - buff.ly/DHp4ZR1

Want to understand how GPT actually works? This book walks you through building a transformer-based model from the ground up.

6) The AI Engineering Bible - buff.ly/8LQipeQ

A complete guide to full-stack AI development. Covers LLMOps, RAG, vector databases, and open-source frameworks.

7) Building Agentic AI Systems - buff.ly/6vnnXgl

Build autonomous AI agents that reason, plan, and act. Essential reading if you're serious about agentic workflows in 2025.

8) Prompt Engineering for Generative AI - buff.ly/vgk6RZ4

Unlock the power of creative generation — from text to image to code — using multimodal prompting and control.

9) Hands-On Large Language Models - buff.ly/WInCgwi

Go beyond theory. Fine-tune and deploy real LLMs like BERT, GPT, and T5 in production-level systems.

10) Prompt Engineering for LLMs - buff.ly/WInCgwi

Learn the art of crafting prompts that make models like GPT-4 do exactly what you want — every single time.

What would you add to this list?