learning_loop.md

Real-time Learning Loop

Overview

Implements continuous learning from user interactions and feedback to improve response quality over time.

Architecture

graph TD
    A[User Interaction] --> B[Process Query]
    B --> C[Generate Response]
    C --> D[Get User Feedback]
    D --> E[Update Knowledge]
    E --> F[Adjust Confidence]
    F --> G[Store Updated Knowledge]
    G --> A

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Key Components

1. Feedback Collection

• Explicit feedback (thumbs up/down)
• Implicit feedback (user rephrasing, follow-up questions)
• Response quality metrics

2. Knowledge Update

• Confidence-based updates
• Versioning of knowledge entries
• Conflict resolution

3. Model Retraining

• Periodic retraining of ML models
• A/B testing of different responses
• Performance monitoring

Implementation Details

Learning Loop Flow

1. Process user query and generate response
2. Collect explicit or implicit feedback
3. Calculate learning signal
4. Update knowledge base
5. Adjust model parameters if needed

Confidence Management

• Starts with lower confidence for new knowledge
• Increases with positive feedback
• Decreases with negative feedback
• Decays over time without reinforcement

Integration with Knowledge Base

graph LR
    A[User Feedback] --> B[Learning Module]
    B --> C[Update Knowledge Base]
    C --> D[Adjust Confidence]
    D --> E[Store Updates]
    E --> F[Improved Responses]

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Performance Considerations

• Implements batch updates for efficiency
• Uses incremental learning where possible
• Implements rate limiting for resource-intensive operations

Related Files

• src/sifu/learning/feedback.py
• src/sifu/learning/updater.py
• src/sifu/learning/confidence.py