withInstructions

Configures optional user-defined instructions that influence learning behavior.

Instructions provide a mechanism for users and applications to guide KARL's learning process without requiring deep knowledge of machine learning algorithms. They act as high-level constraints and preferences that shape how the AI interprets data and generates predictions.

Instruction categories:

• Learning preferences: What types of patterns to prioritize or ignore

• Prediction constraints: Rules about when and how to make suggestions

• Privacy controls: Limitations on data usage and storage

• Context rules: How to interpret interactions in different scenarios

• Adaptation policies: How quickly to respond to changing user behavior

Instruction formats:

val instructions = listOf(
    KarlInstruction.LearningFocus(
        type = "temporal_patterns",
        priority = InstructionPriority.HIGH,
        description = "Focus on time-of-day usage patterns"
    ),
    KarlInstruction.PredictionRule(
        condition = "confidence < 0.7",
        action = "suppress_suggestion",
        description = "Only show high-confidence predictions"
    ),
    KarlInstruction.PrivacyConstraint(
        type = "data_retention",
        value = "30_days",
        description = "Automatically delete old interaction data"
    )
)

Dynamic instruction updates: Instructions can be modified after container creation using:

container.updateInstructions(newInstructions)

This allows for runtime adaptation based on user feedback or changing requirements.

Instruction processing:

• Instructions are validated for syntax and semantic correctness

• Conflicting instructions are resolved using priority ordering

• Changes take effect immediately for new predictions

• Historical data interpretation may be affected by instruction changes

Parameters

See also