getPrediction

Generates a prediction based on current learned patterns and recent context.

This method serves as the primary interface for requesting AI-generated predictions, suggestions, or recommendations from the KARL container. It leverages all learned patterns, current context, and user instructions to provide intelligent suggestions that enhance the user experience.

Prediction generation process:

1. Context gathering: Collect recent interaction data and environmental factors

2. Pattern analysis: Identify relevant learned patterns from the user's history

3. Engine coordination: Request prediction from the learning engine

4. Instruction application: Apply user-defined filters and customization rules

5. Quality validation: Ensure prediction meets confidence and relevance thresholds

6. Result formatting: Package prediction with metadata and alternatives

Context integration and analysis:

• Recent user interactions and behavioral patterns

• Current application state and available options

• Temporal context (time of day, frequency patterns)

• User preferences and customization settings

• Environmental factors and system constraints

Prediction quality assurance:

• Confidence threshold enforcement based on learning maturity

• Relevance filtering to ensure suggestions are actionable

• Diversity mechanisms to avoid repetitive or narrow suggestions

• Safety checks to prevent inappropriate or harmful recommendations

Performance optimization:

• Caching of frequently requested predictions

• Async pre-computation for anticipated prediction scenarios

• Resource-aware processing to maintain UI responsiveness

• Graceful degradation under resource constraints

See also

Throws