Confidence Calibration System

Samira El-Masri

@samira-el-masri

·December 31, 2025

Build an LLM confidence calibration system combining multiple signals with trained calibration curves and reliability metrics.

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Build a confidence calibration system for LLM outputs.

## Use Case
{{use_case}}

## Current Confidence Issues
{{confidence_issues}}

## Calibration Goals
{{calibration_goals}}

Implement calibration:

```python
class ConfidenceCalibrator:
    def estimate_confidence(self, query: str, response: str, context: List[str]) -> float:
        """
        Signals to combine:
        - Token probabilities
        - Self-consistency
        - Context coverage
        - Retrieval scores
        """
        pass
    
    def calibrate(self, raw_confidence: float) -> float:
        """Apply calibration curve"""
        pass
    
    def train_calibrator(self, labeled_data: List[Tuple[float, bool]]) -> None:
        """Train on human-labeled accuracy data"""
        pass
```

Include:
- Multi-signal fusion
- Temperature scaling
- Isotonic regression
- Expected calibration error metrics

Details

Category

Coding

Use Cases

Confidence calibrationUncertainty estimationReliability scoring

Works Best With

claude-sonnet-4-20250514gpt-4o

Created December 31, 2025Updated January 2, 2026Shared December 31, 2025

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