How to use Prompt Regression Tester
Run the same prompt against multiple models (Claude 4.5/4.6/4.7, GPT-5, Gemini 2.5) with your own API keys. Diff outputs, score drift, and catch the silent regressions that ship with provider model updates.
What It Does
Use the calculator with intent
Run the same prompt against multiple models (Claude 4.5/4.6/4.7, GPT-5, Gemini 2.5) with your own API keys. Diff outputs, score drift, and catch the silent regressions that ship with provider model updates.
Teams running production LLM workloads who learned the hard way that 'no breaking changes' from a provider isn't the same as 'no behavior changes'.
Interpreting Results
Diff highlighting matters most. Cosmetic phrasing changes are noise; schema deviations, missing fields, and shifted numeric ranges are signal that your downstream parser may break.
Input Steps
Field by field
- 1
Define
Define a test set: pairs of (prompt, expected output category or schema).
- 2
Run calculation
Run the test set against the current model. Save outputs as the golden set.
- 3
On
On schedule (or after a model upgrade announcement), re-run the same set against the new model.
- 4
Read outputs
Read the diff: per-test pass/fail, plus aggregate similarity scores. Drops in similarity flag potential regressions.
- 5
Investigate
Investigate flagged tests. Some drops are legitimate model improvements; others are subtle behavior changes that need prompt adjustments.
Common Scenarios
Use realistic starting points
Cross-version Claude check
Prompt
Stable extraction prompt
Models
Sonnet 4.5, 4.6, 4.7
Look for schema drift between versions; if 4.7 silently changed the output shape, your parser needs an adapter before you upgrade.
Cross-provider portability check
Prompt
Same prompt
Models
Claude, GPT, Gemini
Verify the prompt is portable. Provider-specific phrasings (XML tags, system roles) often produce different shape on rival providers.
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FAQ
Questions people ask next
The short answers readers usually want after the first pass.
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