engineering · 1 min read
RAG Evaluation: Metrics That Actually Predict Production Success
Recall@k is necessary but insufficient. Here are the four metrics Bhogar AI tracks per knowledge base - and the eval harness that calculates them automatically.
BABhogar AI TeamProduct & Engineering
Most teams ship RAG with a smoke test and a feeling. Production-grade RAG ships with continuous evaluation across four dimensions: context precision, context recall, answer faithfulness and answer relevance.
Why it matters
Each dimension catches a different failure mode. Context precision catches noisy retrieval; recall catches missed retrieval; faithfulness catches hallucination; relevance catches off-topic answers.
How Bhogar AI approaches it
Bhogar AI ships an evaluation harness that calculates all four metrics on a curated dataset, runs nightly against your production KB, and surfaces regressions in a dashboard. LLM-as-judge handles the subjective dimensions.
- Context precision, recall, faithfulness, relevance - out of the box
- LLM-as-judge for subjective dimensions
- Curated dataset versioning and golden answers
- Nightly regression runs with alerts
- Per-prompt and per-model breakdowns
What you get
Customers running continuous RAG eval catch 80%+ of quality regressions before they reach end users.