Production RAG

Who this series is for

This series is for engineers who have built a retrieval-augmented chatbot demo and discovered that it answers confidently and wrongly once real documents are involved. It assumes you can call an LLM API and write Python. The focus is on the unglamorous parts of RAG (ingestion, chunking, retrieval quality) because that is where almost all answer-quality problems originate.

What each chapter covers

• Why RAG? sets out when retrieval is the right tool, when fine-tuning or a longer context window is a better fit, and what a production RAG system has to guarantee beyond a demo.
• Document Ingestion covers parsing PDFs, HTML and office documents without losing structure such as tables and headings, and keeping the index in sync as source documents change.
• Chunking Strategies compares fixed-size, structural and semantic chunking, and shows how chunk size directly changes both what can be retrieved and what the model sees.
• Retrieval & Reranking covers dense, keyword and hybrid search, why a reranker often gives the largest single quality improvement, and how to measure retrieval separately from generation.

How to approach it

Use a real document set you care about rather than a toy dataset, because RAG failure modes only show up on messy data. After each chapter, write down five questions your system gets wrong and check whether the right passage was even retrieved. That habit carries directly into the LLM Evaluation series. Expect roughly ten hours.