Automatic Prompt
Optimization
Watch AI improve its own lead-ranking instructions. The system analyzes mistakes, learns patterns, and iteratively refines the prompt for better accuracy.
Evaluate
Run current prompt on 50 test leads
Analyze Errors
Find false positives & negatives
Generate Fixes
AI suggests prompt improvements
Apply & Repeat
Update prompt, iterate until optimal
Optimization Status
Ready to Optimize
Run a 5-iteration improvement cycle. The system will benchmark against the Evaluation Set and evolve instructions from v1 to v5.
Do nothing to use the pre-loaded default dataset.
Expected columns: Full Name, Title, Company, Employee Range, Rank
Optimization Strategy
Objective
Maximizing F1-Score (Relevance) and NDCG@3 (Ranking Accuracy) on a held-out evaluation set of 50+ labelled leads.
Methodology
Using Textual Gradient Descent. The system identifies specific cases where the prompt failed, generates a "critique", and rewrites instructions to fix edge cases without breaking existing behavior.
Current Active Prompt
This is the live system prompt currently being used to score leads. The optimization engine iteratively improves this text based on evaluation results.