Epic 25-D: Parameter Auto-Tuning & Prompt Optimization

[ohdearquant]

Epic D: Parameter Auto-Tuning & Prompt Optimization

1. Prompt Optimization Algorithms

• Issue D1.1: Bayesian / Grid Search
  • Description: Implement search approaches for exploring prompt templates or hyperparameters (temp, top_p, etc.).
  • Key Tasks:
    • Create a module that systematically tries parameter combinations.
    • Store results with success/failure metrics.
  • Acceptance Criteria:
    • We can run “AutoTunePrompt(...params...)” and see best combos recorded.

2. Automated A/B Testing

• Issue D2.1: Parallel Prompt Experiments
  • Description: Allow multiple variations of a prompt to run concurrently, then compare success rates.
  • Key Tasks:
    • Provide a system for “A vs. B vs. C” prompt versions.
    • Gather user feedback or quantitative metrics to decide winners.
  • Acceptance Criteria:
    • A logged summary: “Prompt B yields 20% better extraction than Prompt A.”

3. UI for Parameter Management

• Issue D3.1: Front-End Prompt Tuner
  • Description: Add a tab or page in the knowledge base UI where advanced users can tweak prompt settings or see recommended values.
  • Key Tasks:
    • Display current best-known hyperparameters.
    • Offer a single-click “test new prompt config.”
  • Acceptance Criteria:
    • Users can visually experiment with prompt setups and see immediate feedback.