Confidence Reporting
Surface the agent's uncertainty alongside its answer so downstream code and users know when to verify — not just what the answer is.
Intent & Description
🎯 Intent
Give the system a usable signal for routing low-confidence answers to fallback or human review.
📋 Context
Your assistant feeds into downstream decisions where acting on a wrong answer costs more than pausing to verify. The agent already produces answers; the question is how to attach a reliable uncertainty signal.
💡 Solution
Produce a confidence label (high/medium/low or numeric) alongside each answer. Derive it from sample variance (self-consistency), evaluator score, retrieval recall, or rubric score. Render in UI; route low-confidence to fallback or human review.
Real-world Use Case
- Downstream code or UI needs to distinguish ‘I know’ from ‘I’m guessing’ on each answer.
- A confidence signal can be derived from sample variance, evaluator score, or retrieval recall.
- Low-confidence answers can be routed to fallback or human review usefully.
Source
📌 TL;DR
Attach a confidence score to every answer. Route low-confidence to humans or fallback. Calibrate empirically — false confidence is worse than admitting uncertainty.
Advantages
- Downstream code can branch on confidence — automated triage without human involvement.
- Users learn when to verify instead of trusting every answer equally.
Disadvantages
- Calibration is empirical and drifts as the model or task distribution changes.
- False confidence (model says ‘high’ but is wrong) remains the dangerous failure mode.