Coverage 100% · Format 90% · Risk 88% · Total 93%
Input covers the declared prompt variables well. Expected shape suggests structured output requirements.
AI
Prompt Test Runner
Run a single prompt against many test cases locally and inspect variable coverage, output-shape readiness, and risk heuristics.
PromptTemplate under review
Paste the prompt you want to sanity-check.
Expected shapeFormat target
Describe the ideal format, such as JSON fields or markdown sections.
Test inputsOne case per line
Add one test case per line so the runner can inspect coverage and risk.
Coverage 100% · Format 90% · Risk 88% · Total 93%
Input covers the declared prompt variables well. Expected shape suggests structured output requirements.
Coverage 100% · Format 90% · Risk 88% · Total 93%
Input covers the declared prompt variables well. Expected shape suggests structured output requirements.
Batch prompt checks
This is a browser-side preflight, not a live model runner. It helps you sanity-check prompt and test design before spending tokens.
Use this tool when
These are the practical situations where this workflow usually earns its keep.
You are still shaping the prompt, schema, trace, eval case, or safety posture and want a fast local iteration loop first.
You need a review-friendly artifact before sending work into a live model, batch eval, or agent integration.
You want to compare or inspect AI workflow material without exposing internal prompts or source text more widely than necessary.
Prompt and output iteration
Local AI tools shorten the cycle between seeing a weakness and tightening the prompt, schema, or answer shape that caused it.
Eval and safety preparation
Teams can build rubrics, adversarial cases, or review datasets before they invest in heavier automation or model-backed test runs.
Trace and workflow debugging
A smaller local surface helps reviewers understand tool-call churn, unsupported claims, context drift, or grounding gaps before they open a larger incident or quality review.
Common mistakes to avoid
These are the checks that usually keep the output useful instead of misleading.
Treating heuristic local checks as definitive proof of model quality or safety.
Testing only polished examples instead of the messy or adversarial inputs users will actually create.
Moving prompts or traces into external systems before checking policy and data handling expectations.
Learn how to use this tool
Run one prompt against many local test cases and inspect coverage and risk heuristics. This guide is aimed at AI workflow design work where teams need clearer prompts, safer reviews, or better eval preparation before spending tokens or shipping behavior.
Read the guideTell us what is missing
If this flow helped only partly, leave feedback so we can understand the missing step or edge case.
Leave feedbackRequest the next tool
Use the wishlist to suggest the next utility, workflow, or improvement that would complete this job to be done.
Open wishlistRelated tools
These tools often appear right before or right after this workflow.
Prompt Studio
Assemble reusable prompts with goals, variables, constraints, and output instructions.
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Compare two prompt versions to inspect changed wording, constraints, and emphasis.
Open toolPrompt Eval Scorecard
Score AI outputs for format, groundedness, safety, and completeness with a local rubric.
Open tool