Confidence Calibration System

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