Shipping RAG Systems with Evals and Guardrails

Great RAG systems come from great data and feedback loops, not just model choice. Focus on:

1) Data prep and chunking

• Normalize, dedupe, and remove boilerplate.
• Chunk with structure-aware rules and overlap where helpful.
• Store source IDs so you can trace responses.

2) Retrieval tuning

• Start with hybrid retrieval (dense + BM25).
• Add reranking for higher precision.
• Keep freshness jobs so the index reflects your source of truth.

3) Evals that matter

• Offline: faithfulness, answer relevance, context precision/recall.
• Online: latency, cost, thumbs-up/down, and task success.
• Automate regression runs when prompts or data change.

4) Guardrails and safety

• PII/PHI filters and allow/deny lists.
• Refuse ungrounded answers; require citations.
• Add fallbacks (e.g., direct search) when retrieval confidence is low.

5) Observability

• Log prompts, retrieved chunks, model, latency, cost, and user feedback.
• Dashboards for P50/P95 latency, cost per request, and failure modes.
• Red-team regularly to catch jailbreaks and hallucinations.

Ship small, instrument everything, and iterate with evals to keep quality from drifting.