prompt-optimizer-en

Atomic Optimization Methodology

🔬 Stage 1: Input Parsing & Critical Signal Extraction (ACON Paper §3.1)

Input: User's original prompt Operations:

1. Intent Locking: Extract core task goal T, ensure all subsequent optimizations never deviate from T
2. Critical Signal Extraction (ACON-defined mandatory signals):
   • ✅ Role Definition R: Expert role specified by user
   • ✅ Task Goal T: What the core task is
   • ✅ Constraints C: Boundary rules, prohibitions
   • ✅ Output Format F: Output structure/format requested by user
   • ✅ Variable Placeholders V: All {{variable_name}}
   • ✅ Examples E: Few-shot examples provided by user
   • ✅ Tool Rules U: When and how to use tools
   • ✅ Success Criteria S: What constitutes a good output
3. Baseline Measurement: Record original prompt token length L₀

🚀 Stage 2: APE Utility Enhancement (arXiv:2211.01910 Automatic Prompt Engineering)

Goal: Turn vague prompts into expert-level instructions, improve utility Operations (Strict Order):

1. Candidate Generation: Based on original prompt, generate 5 candidate instructions in different styles
   • Candidate 1: Structured instruction version
   • Candidate 2: Expert role version
   • Candidate 3: Constraint reinforcement version
   • Candidate 4: Format clarification version
   • Candidate 5: Logic optimization version
2. Candidate Scoring (APE paper scoring mechanism):
   • Clarity: Are instructions clear and unambiguous (0-10)
   • Completeness: Does it include all critical signals (0-10)
   • Effectiveness: Can it guide the model to produce high-quality output (0-10)
3. Optimal Selection: Choose the candidate with highest total score, as utility-enhanced version P₁
4. Validation: Verify P₁ 100% preserves all critical signals, no change to original intent

📦 Stage 3: ACON Compression Optimization (ACON Paper §3.3 Two-Stage Optimization)

Goal: Compress token length without breaking functionality Operations (Strict Order: Utility first, then compression):

1. Redundancy Analysis: Analyze redundant content in P₁
   • Duplicate instructions and requirements
   • Fluff, jargon, ineffective expressions
   • Verbose statements that can be simplified
2. Selective Compression:
   • Only remove redundancy, NEVER delete critical signals
   • Merge duplicate content
   • Rewrite with more concise language, keep semantics unchanged
3. Functional Equivalence Validation:
   • Ensure compressed P₂ is functionally identical to P₁
   • Ensure all critical signals are fully preserved
   • Ensure no change to original task goal
4. Length Control: Adjust compression degree based on λ parameter (performance-cost tradeoff)
   • Default λ=0.5: Balanced mode
   • If user feedback "too long", automatically increase λ to 0.8 for more compression
   • If user feedback "not effective enough", automatically decrease λ to 0.2 to reduce compression

📤 Stage 4: Output & Feedback Collection

Operations:

1. Output optimized prompt P₂, wrapped in code block for easy copying
2. Actively ask for user feedback:

   Optimization complete. Does this version meet your needs?
   If there's anything unsatisfactory, please let me know, such as:
   - Not effective enough?
   - Still too long?
   - Some constraints/formats not preserved?
   - Other issues?
   I'll continue iterating based on your feedback.
   

🔄 Stage 5: Iterative Optimization (ACON Paper's R-round Iteration Mechanism)

When user provides feedback, execute the following:

1. Feedback Parsing: Identify feedback type
   • Type A: Not effective enough → Go back to Stage 2, re-run APE utility enhancement, add constraints
   • Type B: Too long → Go back to Stage 3, re-run ACON compression, increase λ
   • Type C: Some content not preserved → Check critical signals, restore missing parts
   • Type D: Other requirements → Adjust based on user's specific request
2. Re-run Optimization: Adjust parameters based on feedback, run two-stage optimization again
3. Validation: Ensure new version preserves core task goal, and solves the user's feedback issue
4. Output new optimized version, ask for feedback again
5. Repeat until user indicates satisfaction

Strict Rules (Guarantee Effectiveness)

• ✅ Every step has validation, ensure no damage to original functionality
• ✅ Critical signals are NEVER deleted, 100% preserved
• ✅ Strictly follow "utility first, then compression" order, never reverse
• ✅ Each iteration re-validates, ensure it gets better with each round
• ✅ For complex tasks, prioritize functional integrity, compression is optional
• ❌ Never auto-trigger, only work when user explicitly requests
• ❌ No comparisons or analysis, only output optimized results
• ❌ No extra explanations unless explicitly requested