MinConfidence

Sets the minimum confidence threshold for prediction generation and presentation.

This instruction controls the quality bar for predictions by specifying the minimum confidence level required before a prediction is generated or presented to the user. It helps balance between providing helpful suggestions and avoiding noise from uncertain or unreliable predictions.

Confidence threshold effects and implications:

Quality vs. quantity trade-offs:

• Higher thresholds: Fewer but more reliable predictions

• Lower thresholds: More predictions but potentially less accurate

• Optimal thresholds: Balanced based on user tolerance for incorrect suggestions

Dynamic threshold considerations:

• Learning maturity: Lower thresholds acceptable for new systems building confidence

• Domain criticality: Higher thresholds for high-stakes decisions or actions

• User expertise: Experienced users may prefer lower thresholds for more options

• Context sensitivity: Different thresholds for different prediction types or scenarios

Threshold range interpretation:

• 0.0 - 0.3: Very permissive, includes experimental and exploratory predictions

• 0.3 - 0.5: Moderate filtering, balances exploration with reliability

• 0.5 - 0.7: Conservative approach, focuses on well-supported predictions

• 0.7 - 1.0: Very conservative, only high-confidence predictions are shown

Implementation behavior:

• Predictions below the threshold are suppressed and not returned to applications

• The instruction affects all prediction types unless overridden by type-specific instructions

• Confidence calibration may adjust actual thresholds based on historical accuracy

• Alternative suggestions may still be provided even if primary prediction is suppressed

Adaptive threshold strategies:

• Learning-based: Automatically adjust thresholds based on prediction accuracy feedback

• Context-aware: Use different thresholds for different application scenarios

• User-adaptive: Learn individual user tolerance for prediction accuracy

• Time-varying: Adjust thresholds based on system maturity and data availability

Example usage:

val instructions = listOf(
    KarlInstruction.MinConfidence(0.6f),  // Only show predictions with 60%+ confidence
    // Other customization instructions...
)

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