Prompt Optimizer

Optimizes system prompts by applying research-backed prompt engineering patterns. Human-in-the-loop phases: understand, plan, propose changes, receive approval, then integrate.

Purpose and Success Criteria

A well-optimized prompt achieves:

1. Behavioral clarity: Agent knows exactly what to do in common cases and edge cases
2. Appropriate scope: Complex tasks get decomposition; simple tasks don't trigger overthinking
3. Grounded changes: Every modification traces to a specific pattern with documented impact

Optimization is complete when:

• Every change has explicit pattern attribution from the reference document
• No section contradicts another section
• The prompt matches its operating context (tool-use vs. conversational, token constraints)
• Human has approved both section-level changes and full integration