engineering · 1 min read
Confidence Scoring: Knowing When Your RAG Answer Is Weak
A RAG system that knows when it does not know is dramatically more useful than one that always sounds confident. Here is how to score and act on confidence.
BABhogar AI TeamProduct & Engineering
Calibrated confidence is the single biggest predictor of "do users trust this AI". Confident-when-wrong is a trust killer; humble-when-uncertain is a trust builder.
Why it matters
Most RAG systems output the same self-assured prose regardless of retrieval strength. Production systems should grade their own answers and either route to a human or abstain when confidence is low.
How Bhogar AI approaches it
Bhogar AI ships per-answer confidence scoring derived from retrieval signals (top-k similarity, hit count) and self-evaluation by the answer model. Configurable thresholds drive abstain, escalate, or proceed actions.
- Composite confidence score per answer
- Configurable abstain / escalate / proceed thresholds
- Telemetry on confidence distribution
- Per-tenant calibration via evaluations
- API exposes confidence to downstream apps
What you get
Support deployments using confidence-based escalation cut hallucination-driven complaints 60-80% with negligible deflection-rate impact.