Retrieval-augmented generation looks easy in a demo and hard in production. Here's what our pods learned shipping it for real.
The demo-to-production gap
A RAG prototype takes an afternoon. A RAG system you can trust takes real engineering. The difference is everything that happens around the model.
Five lessons
- Retrieval is the product. Most quality problems are retrieval problems, not generation problems. Invest in chunking, embeddings, and ranking first.
- Evaluate continuously. Build an eval set from real questions on day one and run it on every change.
- Ground and cite. Always show sources. It builds trust and makes failures debuggable.
- Watch freshness. Stale indexes quietly degrade answers. Automate re-indexing.
- Budget for cost. Token and infra costs sneak up. Measure cost-per-answer like a first-class metric.
The takeaway
RAG isn't a model you bolt on. It's a system you engineer — retrieval, evaluation, observability, and cost control working together.
Comments
No comments yet — be the first to share your thoughts.