Tools: What we learned from 100+ production RAG deployments (free 118-page handbook)

We’ve been building RAG systems for a while and wanted to share a resource we just published. It’s a 118-page handbook covering the patterns that separate prototype RAG from production RAG. If you’re building RAG right now, here are the problems this covers: Your vector search returns “close enough” results instead of exact matches. The handbook covers hybrid retrieval that runs semantic and keyword search in parallel. Your chunking splits documents in weird places. It covers semantic chunking, code-aware chunking using ASTs, and parent-child structures that keep context intact. You have no idea if your retrieval is actually good. It covers evaluation frameworks that work without manually labeling test data. Your costs keep growing and you can’t figure out why. It covers production observability that traces every step of your pipeline. It also has dedicated chapters on building RAG for specific domains: code generation, text-to-SQL, legal search, and medical knowledge retrieval. Each one has different failure modes that generic approaches miss. Free PDF - https://shorturl.at/rRXXP Would love to hear what problems others are hitting with production RAG, always helps to know what to cover next. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Your vector search returns “close enough” results instead of exact matches. The handbook covers hybrid retrieval that runs semantic and keyword search in parallel.
- Your chunking splits documents in weird places. It covers semantic chunking, code-aware chunking using ASTs, and parent-child structures that keep context intact.
- You have no idea if your retrieval is actually good. It covers evaluation frameworks that work without manually labeling test data.
- Your costs keep growing and you can’t figure out why. It covers production observability that traces every step of your pipeline.