RAG in Production: Retrieval, Evals, and the Traps We Avoid
Moving a prototype RAG feature into production means grounding, evaluation loops, and operational guardrails—not prompt tweaks alone.
Retrieval-Augmented Generation is the fastest path to useful LLM features over private documents—but the moment you expose it to real users, “mostly right” stops being acceptable.
Production RAG needs versioning, monitoring, and evaluation datasets that reflect actual questions. Without those, you are shipping a demo that drifts the first time your docs change.
Chunking and metadata are the product
Poor chunks produce confident nonsense. We invest in structure: headings, section boundaries, tables handled intentionally, and metadata that helps retrieval discriminate between similar pages.
For mixed corpora (PDFs, tickets, wikis), normalization pipelines matter. Clean text extraction beats flashy embedding models when the source material is messy.
Grounding checks and citations
Users and compliance teams want traceability. When possible, expose citations to source snippets and train the UI to show uncertainty when retrieval scores are weak.
If an answer is not supported by retrieved context, the system should refuse or ask a clarifying question—especially for regulated domains.
Evals that match real usage
Golden sets should include paraphrases, multilingual prompts if relevant, and adversarial cases: out-of-scope questions, contradictory docs, and stale content.
Automate regression runs on PRs that touch retrieval or prompts. Track latency and cost per query class; optimize the hot paths first.
Operations: cache, quotas, abuse
LLM workloads need rate limits, bot protection on public endpoints, and caching for repeated queries. Observability should capture retrieval hits, token usage, and failure modes—not just HTTP 500s.
Plan for model upgrades as migrations: snapshot prompts, compare evals, and roll out gradually Canary-style when provider behavior shifts.