Mar 2026· 7 min· By StacX24 Team
Building a RAG system that stays accurate as it grows
Retrieval quality degrades quietly as your corpus changes. Here's how to catch it before your users do.
RAGEvals
A RAG system that's accurate on launch day is easy. One that's still accurate after the corpus has tripled and the questions have shifted is the actual engineering problem.
Why accuracy decays
- New documents change what retrieval surfaces for old queries.
- Duplicates and near-duplicates dilute relevance.
- The questions users ask drift away from what you tested.
None of this throws an error. The system just gets quietly worse, and you find out from a complaint.
What keeps it honest
- A living eval set — real questions with known-good answers, grown as usage reveals new patterns.
- Groundedness scoring — measure whether answers are actually supported by the retrieved sources, not just plausible.
- Regression gates in CI — re-index, re-run the evals, and block the deploy if retrieval quality drops.
Treat retrieval as a measured component
Retrieval quality is a number you can track on a dashboard and alert on. Once it's measurable, "is the assistant still good?" stops being a matter of opinion — and your RAG system earns the trust to stay in production.
More insights
- Write the evals before you write the promptsIf you can't measure whether the model is doing the right thing, you're not engineering — you're guessing.
- What observability actually means for AI agentsLogs, traces, and metrics for systems that make decisions — so when an agent does something strange, you can see why.