Building RAG Assistants That Actually Work in Production

Everyone can build a RAG demo in an afternoon. Far fewer can build one a CFO will trust with customer-facing answers. The gap is all in the engineering around the model.

Retrieval quality is everything

If the right context never reaches the model, no amount of prompt engineering saves you. Invest in chunking strategy, hybrid search, and re-ranking before you touch the prompt.

• Chunk by semantic boundaries, not fixed token counts
• Combine vector search with keyword search (hybrid retrieval)
• Re-rank candidates before passing them to the model
• Cite sources so answers are verifiable

Evaluate continuously

Build an evaluation set of real questions and grade every change against it. Without measurement, 'improvements' are just vibes.

A RAG system without an eval harness is a science experiment, not a product.