One page, one function: build a repeatable prompt test plan instead of judging one answer.
Free prompt test generator
Prompt Test Generator
Turn a prompt idea into a repeatable test outline with fixtures, expected behavior, scoring priorities, failure labels, reviewer mode, and a retest rule.
Start here
Generate a prompt test outline
Choose the task, number of fixtures, scoring priority, and reviewer mode. The output gives you a test outline you can copy into an eval doc.
Useful when a prompt must be stable across cases, models, source updates, and reviewer expectations.
Copy the generated outline into a prompt changelog, eval tracker, QA doc, or benchmark run log.
Prompt testing should use evidence, not taste
A prompt can look good on one example and still fail on the next ten. Prompt evaluation works better when the team freezes model settings, reuses the same inputs, scores the same dimensions, and labels failures consistently. The Prompt Test Generator creates that structure before the prompt is treated as stable.
The goal is not to make prompt work heavy. The goal is to keep prompt changes from silently breaking source faithfulness, format, refusal behavior, tone, or safety expectations after a model or input changes.
What a good fixture set includes
- Normal cases that represent the work users actually do.
- Edge cases with missing data, ambiguous source text, or unusual formatting.
- Adversarial cases that try to force unsupported claims or unsafe behavior.
- No-answer cases where the correct behavior is refusal, escalation, or asking for more context.
- Regression cases from past failures that must not return.
Scoring dimensions to consider
- Correctness: does the answer solve the task without adding unsupported detail?
- Citation quality: are claims tied to the right source?
- Format: does output preserve required structure?
- Safety: does the prompt refuse or escalate when needed?
- Reviewer effort: how hard is it to verify the result?
How to run a prompt test
- Generate the outline. Start with 5 or 10 fixtures if the workflow is new.
- Write real inputs. Use actual source excerpts, pull request examples, RAG questions, rewrite briefs, or localization samples.
- Freeze variables. Record the prompt version, model, temperature, retrieval settings, and source set.
- Score each result. Use the same rubric and failure labels across prompt versions.
- Keep a retest trigger. Retest after prompt changes, model changes, source changes, or repeated reviewer overrides.
Related guides
Frequently asked questions
Why test prompts with fixtures?
Fixtures let you compare prompt versions against the same inputs, scoring rules, and failure labels instead of judging one impressive answer.
How many prompt test fixtures should I start with?
Start with five to ten representative cases, then add edge cases, adversarial cases, and no-answer cases as failures appear.
When should I retest a prompt?
Retest after prompt edits, model changes, source changes, repeated reviewer overrides, or a production failure tied to the prompt.
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