AI engineering learning path
RAG & retrieval
Turn documents into retrievable evidence and evaluate the answers they support.
Suggested background: basic Python, HTTP, and JSON. The examples focus on engineering boundaries and can be practiced locally.
From having documents to finding evidence.
From having documents to finding evidence.
Chapters
The smallest RAG loop: retrieve, cite, answer
Separate retrieval from generation and use offline keyword retrieval to understand provenance and missing evidence.
Chunk documents for meaning before tuning an index
Understand chunking, overlap, metadata, and approximate vector indexes using a small windowing example.
Evaluate retrieval and answers separately in RAG
Use Recall@k, evidence support, and unanswerable cases to locate failures in a RAG pipeline.