Building a Production RAG Pipeline: Lessons Learned

RAG has become the go-to architecture for AI apps that need private data. But moving from prototype to production involves solving challenges most tutorials skip.

The Chunking Problem

Too small chunks lose context. Too large exceed token limits. We landed on 512-token chunks with 50-token overlap.

Lessons

1. Monitor retrieval metrics
2. Implement feedback loops
3. Cache common queries — reduced costs 40%