Use case · RAG evaluation
Is your RAG pipeline actually grounded?
RAG evaluation in EvalGlass is not a packaged benchmark — it is project-specific checks you and your coding agent author from your own data: retrieval faithfulness, answer quality, and the trace evidence behind each answer, run locally and returned as a bounded scorecard.
Evaluate my RAG answers for faithfulness and grounding against my examples.
What it checks
Retrieval faithfulness · answer quality vs. your validated reference data · context grounding · trace evidence per answer. These are checks shaped around your product's behavior — not a generic leaderboard or a magic quality number.
What it will not claim
A green result means the checks you approved passed on the examples you supplied — never that your RAG system is correct or complete. Judge-assisted faithfulness metrics produce evidence, not authority: they cannot gate until you calibrate them against your own labels. A run stays informational until you promote a gate on a threshold you approve. No false green →
Ask your coding agent.
Evaluate my agentic app using EvalGlass.