Resources

Curated, not exhaustive. The best books, papers, courses, tools, and APIs for AI Engineering — hand-picked for practical learning.

Books

📚

Designing Machine Learning Systems

Production ML systems by Chip Huyen — essential for AI engineers.

📚

Build a Large Language Model (From Scratch)

Hands-on LLM implementation by Sebastian Raschka.

📚

AI Engineering

Chip Huyen's guide to building production AI applications.

Papers

📄

Attention Is All You Need

The original Transformer paper — foundational reading.

📄

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

The original RAG paper by Lewis et al.

📄

ReAct: Synergizing Reasoning and Acting in Language Models

Foundational agent pattern combining reasoning and tool use.

Courses

🎓

DeepLearning.AI — Generative AI for Everyone

Andrew Ng's accessible introduction to GenAI.

🎓

LangChain Academy

Official LangChain and LangGraph courses.

🎓

Full Stack LLM Bootcamp

Intensive LLM engineering bootcamp by FSDL.

Tools

🔧

LangSmith

LLM application observability and evaluation platform.

🔧

Ollama

Run open-source LLMs locally with a simple CLI.

🔧

Pinecone

Managed vector database for production RAG.

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vLLM

High-throughput LLM inference engine.

APIs

🔌

OpenAI API

GPT-4, embeddings, fine-tuning, and assistants API.

🔌

Anthropic Claude API

Claude models with strong reasoning and safety.

🔌

Google Gemini API

Gemini multimodal models and embeddings.