7 min read
RAG pipelines that survive production (not just demos)
Practical lessons on chunking, retrieval quality, grounded answers, and evaluation for production RAG systems.
- RAG
- AI Engineering
- LLM
Demo RAG is easy: dump PDFs into a vector store and ask questions. Production RAG fails on ambiguous queries, weak chunking, and answers that sound confident while being wrong.
What actually matters
- Chunk for meaning, not arbitrary token windows
- Retrieve more than you show — then re-rank
- Force citations / grounded structure in the response
- Log failures: no-hit queries are gold for iteration
Where I use this
In SeedheDocs, retrieval has to respect assignment context and report structure. In Relayt, each tenant’s chatbot is trained on that company’s docs and site crawl — isolation and grounding are non-negotiable.
If your RAG only works on happy-path prompts, it is not ready. Ship evaluation loops early.