Confidence Score
A numeric indicator of the AI's certainty or reliability in its response, used in evaluation metrics and threshold-based decisions.
Overview
Confidence scores represent how certain an AI system or evaluation metric is about a particular output or assessment. In LLM testing, confidence scores appear in metrics, judge evaluations, and threshold-based pass/fail decisions.
Confidence in Different Contexts
When evaluating metrics, confidence represents how certain the judge is about its evaluation. This helps you understand whether the metric is making clear-cut decisions or struggling with borderline cases. Response confidence refers to the AI's certainty about its own answer, which can be useful for identifying when the system might need human review or additional context.
Testing Confidence Calibration
Confidence calibration testing examines whether the AI's expressed confidence aligns with actual accuracy. A well-calibrated system should be correct 80% of the time when it expresses 80% confidence. Testing this alignment helps identify when your system is overconfident (expressing high certainty while making errors) or underconfident (expressing doubt about correct answers). You can also evaluate how well the system expresses confidence through natural language, such as using phrases like "I'm not entirely sure" or "This is definitely correct."
Confidence Thresholds
Setting appropriate pass/fail thresholds based on confidence scores requires balancing risk and coverage. Higher thresholds catch fewer items but with greater certainty, while lower thresholds capture more cases but with increased false positives. The threshold you choose significantly impacts your system's behavior—too strict and you'll reject valid responses, too lenient and you'll accept poor quality outputs.
Confidence Calibration Issues
Many AI systems exhibit overconfidence, expressing high certainty even when making mistakes. This can be particularly dangerous in high-stakes applications where users may trust confident-sounding but incorrect information. Conversely, some systems are underconfident, hedging unnecessarily even when providing accurate information. Both patterns need attention: overconfidence requires stricter validation before accepting responses, while underconfidence may need adjustment to avoid unnecessarily triggering fallback behaviors.
Testing Confidence
Effective confidence testing involves comparing confidence scores against actual accuracy across many examples. You should test calibration by grouping predictions by confidence level and measuring actual accuracy within each group. Edge case testing helps identify scenarios where the system should express uncertainty, such as ambiguous inputs or requests outside its training data. Regular monitoring of confidence score distributions reveals whether your system consistently expresses appropriate levels of certainty.
Best Practices
When implementing confidence-based systems, match threshold criticality to use case importance. Critical features like medical advice or financial decisions warrant higher confidence thresholds, while exploratory features can use more lenient settings. Consider the cost of different error types—false negatives versus false positives—and adjust thresholds accordingly. Always validate threshold choices with real data rather than guessing, and monitor system performance over time as confidence patterns may shift.
For the AI system itself, prioritize honesty about limitations and uncertainty over appearing confident. Ensure confidence expressions align with actual correctness through calibration testing. Use appropriate qualifiers when uncertain rather than making definitive statements. Most importantly, avoid false confidence by never sounding certain when the system is actually guessing or extrapolating beyond its knowledge.
Testing should verify that confidence matches accuracy across different scenarios and input types. Find optimal pass/fail points through experimentation with real data. Pay special attention to edge cases where the system should express appropriate uncertainty. Track confidence score distributions over time to detect drift or calibration issues that need addressing.