You get:
- choosing the wrong version because you tested on easy examples
- no systematic way to compare prompts across multiple dimensions
- team debates about which prompt is better (no data)
- improvements that actually hurt performance on edge cases
- no learning from prompt iterations (what worked and why)
But prompt comparison needs structure:
- test set: diverse inputs covering typical, edge, and adversarial cases
- metrics: accuracy, latency, cost, consistency
- trade-offs: Version A is better on X but worse on Y
- explanation: why one version outperforms (not just which)
- recommendation: which to deploy and under what conditions
Without comparison, you iterate blindly.
This prompt provides structured comparison across test inputs.
Assume the role of a prompt evaluation scientist who compares prompt versions. Your task is to compare two prompt versions and recommend which to deploy. Generate: 1. PROMPT VERSIONS SUMMARY - Version A: [description of key changes from baseline] - Version B: [description of key changes from baseline] - Baseline (if applicable): [original version] 2. TEST INPUTS USED - List of test inputs (or describe diversity) 3. COMPARISON MATRIX (per test input) | Test Input | Version A Output | Version B Output | Winner | Notes | |------------|------------------|------------------|--------|-------| | [input 1] | [output] | [output] | A/B/Tie | [why] | 4. AGGREGATE METRICS - Accuracy: A: X/10, B: Y/10 - Consistency (same output across runs): A: High/Med/Low, B: High/Med/Low - Verbosity (output length): A: [avg], B: [avg] - Failure cases: A: [list], B: [list] 5. TRADE-OFF ANALYSIS - Where A wins (and why) - Where B wins (and why) - Any critical failure that disqualifies a version 6. RECOMMENDATION - Which version to deploy (A / B / Neither / Both for different cases) - Rationale (2-3 sentences) - What to monitor after deployment INPUTS: Version A prompt: [PASTE] Version B prompt: [PASTE] Test inputs (5-10 diverse examples): [PASTE INPUTS — OR DESCRIBE CATEGORIES TO GENERATE] Success criteria (what "better" means): [E.G., "Higher accuracy on edge cases, even if slower"] Model: [GPT-4 / CLAUDE / GEMINI] RULES: - If test inputs aren't provided, generate them covering typical, edge, and adversarial cases - Flag if prompts produce identical outputs (no meaningful difference) - Consider cost and latency if relevant to your use case - Note if a version is more consistent but less creative (trade-off) - Don't recommend a version that fails catastrophically on any test input
- Run this before deciding which prompt version to deploy — don’t guess.
- Use at least 10 test inputs covering typical, edge, and adversarial cases.
- Run each version multiple times per input to measure consistency.
- Pay attention to trade-offs — version A may be better overall but worse on your most important case.
- Save comparison reports to build institutional knowledge about what works.
Version A prompt:
“Summarize this text in 2-3 sentences.”
Version B prompt:
“You are a professional editor. Summarize the following text in exactly 2-3 sentences. Focus on main argument and key supporting point only. Do not include examples or minor details. Output format: Summary: [text]”
Test inputs:
“5 diverse paragraphs — one short, one long, one with multiple arguments, one with no clear argument, one technical”
Success criteria:
“More concise summaries that capture main argument without extraneous details”
This framework improves outcomes by forcing:
- structured test inputs (diversity, not convenience)
- side-by-side comparison (per input, not aggregate only)
- multiple metrics (accuracy, consistency, verbosity)
- trade-off analysis (where each version wins)
- clear recommendation (deploy A, B, or neither)
Great prompt comparison doesn’t just declare a winner — it tells you why one version is better and for what cases.
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