You get:
- reasoning chains with redundant or unnecessary steps
- paying for tokens that don’t improve the answer
- slower responses from overly verbose reasoning
- context window filled with fluff instead of useful reasoning
- no systematic way to find the minimal effective chain
But minimal chains have properties:
- necessary steps: cannot be removed without breaking correctness
- redundant steps: can be removed with no impact
- compressible steps: can be stated more concisely
- parallelizable steps: can be reordered or combined
- implicit steps: don’t need to be stated (model can infer)
Without optimization, you waste tokens.
This prompt finds the minimal reasoning chain that still produces correct answers.
Assume the role of a reasoning efficiency engineer who compresses reasoning chains. Your task is to find the minimal set of steps needed to solve a problem correctly. Generate: 1. ORIGINAL REASONING CHAIN - All steps as originally written 2. STEP NECESSITY ANALYSIS | Step | Content | Necessary? (Yes/No) | Why | |------|---------|---------------------|-----| | 1 | [step] | Yes/No | [reason] | | 2 | [step] | Yes/No | [reason] | | ... | ... | ... | ... | 3. REDUNDANT STEPS (can be removed) - List with rationale 4. COMPRESSIBLE STEPS (can be shortened) - Original vs. compressed version 5. MINIMAL REASONING CHAIN - Only necessary steps, each stated concisely 6. VALIDATION - Does the minimal chain still produce the correct answer? (Yes/No — test required) - Token savings: [X] tokens saved ([Y]% reduction) - Accuracy risk: (Low / Medium / High) 7. DEPLOYMENT RECOMMENDATION - Use minimal chain for production (if low risk) - Keep original for complex cases - Test on edge cases before deploying INPUTS: Original reasoning chain (full): [PASTE THE REASONING WITH STEPS] Problem type: [ARITHMETIC / LOGIC / COMMON SENSE / PLANNING / OTHER] Correct answer (for validation): [THE KNOWN CORRECT ANSWER] Model: [GPT-4 / CLAUDE / GEMINI] RULES: - A step is necessary if removing it changes the answer or breaks the reasoning - A step is redundant if the model can infer it without explicit statement - Compressed steps must preserve meaning (no shortcuts that change the logic) - Test the minimal chain before deploying (some necessary steps are not obvious) - Flag if minimal chain reduces accuracy for edge cases
- Run this on reasoning chains that will be used at high volume — token savings add up fast.
- Test the minimal chain on at least 10 edge cases before deploying.
- Don’t over-optimize for low-stakes tasks (saving tokens isn’t worth accuracy risk).
- Save the minimal chain as a template for similar problems.
- Re-optimize when you switch to a new model (different models need different steps).
Original reasoning chain:
“Step 1: The problem asks for 15% of 280. Step 2: 15% means 15 per 100. Step 3: So 15% of 280 equals (15/100) × 280. Step 4: 15/100 simplifies to 0.15. Step 5: 0.15 × 280 = (0.15 × 200) + (0.15 × 80). Step 6: 0.15 × 200 = 30. Step 7: 0.15 × 80 = 12. Step 8: 30 + 12 = 42. Step 9: Therefore, 15% of 280 is 42.”
Problem type:
“ARITHMETIC”
Correct answer:
“42”
This framework improves outcomes by forcing:
- step necessity analysis (what actually matters)
- redundancy identification (what can be removed)
- step compression (what can be shortened)
- validation testing (does it still work?)
- token savings calculation (quantified benefit)
Great minimal step optimization doesn’t sacrifice accuracy — it removes waste while preserving correctness.
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