Your RAG demo finds one answer. Real users will show you where it falls short.

Retrieval-augmented generation for developers

RAG, embeddings, vector search and reranking for AI applications, with Python examples and TypeScript notes

An engineer types "How do I roll back billing-api?" and your RAG demo finds the right handbook section. Now point it at a company's whole wiki, its runbooks and its tickets, with real users asking. When the answers slip, the work is finding which stage of the pipeline failed, what to change, and how to know the change helped.

One system grows from a handful of pages to all of that, and its retrieval gets rebuilt a stage at a time. It starts by measuring retrieval before building any, then turns HTML and PDF into clean sections, chunks them and picks an embedding model. You'll see why BM25 and keyword search still matter, where the vectors live, and how hybrid search with rank fusion and a reranker sit on top. Later chapters rewrite queries and assemble a context that gets cited answers or honest refusals. Agentic retrieval and GraphRAG come with the question of when they pay. The last stretch is production, with permissions, freshness, latency and cost kept under control.