KarlInstruction

Represents user-defined instructions that customize KARL's learning and prediction behavior.

KarlInstruction provides a mechanism for users and applications to influence how the KARL system processes data, generates predictions, and adapts its behavior. Instructions act as high-level constraints and preferences that guide the AI without requiring deep knowledge of machine learning algorithms or implementation details.

Instruction architecture and design:

Sealed class hierarchy:

• Provides type safety and compile-time validation for instruction types

• Enables exhaustive pattern matching for instruction processing

• Allows for easy extension with new instruction types

• Ensures consistent instruction handling across different components

Instruction categories and purposes:

Data processing instructions:

• Control which types of interactions are included in learning

• Specify data preprocessing and filtering preferences

• Define privacy constraints and data retention policies

• Configure feature extraction and pattern recognition parameters

Learning behavior instructions:

• Adjust learning rates and adaptation speed

• Specify focus areas and pattern priorities

• Control model complexity and resource usage

• Define convergence criteria and stopping conditions

Prediction customization instructions:

• Set confidence thresholds for suggestion generation

• Specify prediction types and content filters

• Configure presentation styles and interaction modes

• Define fallback behaviors for uncertain predictions

Privacy and security instructions:

• Control data collection scope and granularity

• Specify encryption and anonymization requirements

• Define data sharing and export restrictions

• Configure audit trails and compliance reporting

User experience instructions:

• Customize notification frequency and timing

• Specify interaction modes and presentation preferences

• Configure accessibility and usability adaptations

• Define personalization boundaries and constraints

Instruction processing and application:

Validation and consistency:

• Instructions are validated for syntax and semantic correctness

• Conflicting instructions are resolved using priority rules

• Invalid instructions are rejected with descriptive error messages

• Instruction changes are applied atomically to prevent inconsistent state

Component coordination:

• Instructions are distributed to all relevant container components

• Each component interprets and applies instructions within its domain

• Instruction updates trigger reconfiguration of affected systems

• Cross-component instruction effects are coordinated and synchronized

Dynamic updates and persistence:

• Instructions can be updated at runtime without container restart

• Instruction changes take effect immediately for new operations

• Instructions are persisted with container state for session continuity

• Instruction history may be maintained for debugging and analysis

Performance and optimization:

• Instructions are preprocessed and compiled for efficient runtime application

• Frequently used instructions are cached for fast access

• Instruction evaluation is optimized for minimal performance impact

• Complex instructions may be simplified or approximated for efficiency

Extensibility and custom instructions:

The sealed class design allows for easy extension with new instruction types:

sealed class KarlInstruction {
    // Existing instructions...

    // Custom instruction examples:
    data class LearningFocus(val domain: String, val priority: Float) : KarlInstruction()
    data class PrivacyLevel(val level: String, val restrictions: List<String>) : KarlInstruction()
    data class ResponseStyle(val style: String, val verbosity: Int) : KarlInstruction()
    data class TimeConstraint(val maxResponseTime: Long, val degradeGracefully: Boolean) : KarlInstruction()
}

Best practices for instruction design:

• Keep instructions simple and focused on single concerns

• Use clear, descriptive names and parameter types

• Provide reasonable default values for optional parameters

• Include validation logic for parameter ranges and constraints

• Document expected behavior and interaction effects

• Consider performance implications of complex instructions

Thread safety and concurrency:

• Instructions are immutable and safe for concurrent access

• Instruction updates are atomic and consistent across all components

• No shared mutable state in instruction implementations

• Thread-safe instruction processing and validation

Inheritors