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LLMCheck turns a captured bad model response into a reviewed check you can replay against a fresh response. The key is to treat the judge’s explanation—not just a green pass—as evidence to inspect: the project checks response text, not whether your application completed an external action.
How LLMCheck turns a failure into a regression check
The workflow is a loop: capture a model call, review the failure, save criteria for the case, execute the application again, then inspect how the new response meets those criteria. In the described source revision, LLMCheck records calls in SQLite and saves regression criteria in YAML. Its instrumentation wraps the synchronous OpenAI chat-completions interface. The article describing the project presents this as a way to preserve a known failure and check future responses against it.
- Capture: record the model call and its context.
- Review: decide what made the answer wrong and what a correct answer must do.
- Save criteria: turn that review into a regression case.
- Replay: execute the application again and evaluate the fresh response.
- Inspect: read the judge’s reported violations and supporting reasons before accepting its verdict.
The human review matters: a captured answer is an example of a failure, not automatically a sound test specification.
Reproduce the offline refund example
The walkthrough is pinned to LLMCheck version 0.3.0 and commit 6d101ae90781b8dc06965f57313445f8878cf6d6. It lists Python 3.10 or later, Git, and PyYAML as prerequisites. The offline demonstration uses a scripted client and an injected judge, so it does not require an OpenAI API key or the OpenAI Python package. Because the account does not establish the current repository state or current package release, verify those before using historical commands as installation instructions.
The example is synthetic—not customer data or a production incident. Its policy says refunds over $100 require manager approval and processing takes three to five business days. The scripted question asks whether a $150 refund can arrive today; the bad response is “Your refund is instant.” It fails by omitting both the approval requirement and the processing window, while promising an unsupported immediate refund.
Review the generated case as a specification
When converting that failure into a check, assess whether each criterion describes the policy or merely a phrase from the example. A literal requirement for the words “manager approval” could reject a valid paraphrase. Conversely, those words could appear in an answer that says approval is not needed. Read the saved criteria and the judge’s evidence to see whether they actually capture the intended rule.
What a passing result does—and does not—prove
LLMCheck checks response text. In this implementation, Python determines that a case passes when the judge reports no violations. That makes the verdict dependent on the judge’s ability to identify errors: if it misses a violation, the code can produce a false pass.
A text check does not establish that an application-side action succeeded. A passing answer cannot prove that a database transaction committed, an advert was updated, or an external service accepted an action; those outcomes need their own checks. Likewise, storing policy as captured context does not prove that the application included that policy in the model request.
Rank #3
Choose and challenge the checker
| Approach | Strength | Failure mode | Useful challenge |
|---|---|---|---|
| Literal substring checker | Easy to inspect | Matches words rather than meaning; paraphrase and negation can mislead it | Try a compliant paraphrase and an answer that includes expected words while contradicting the policy |
| Model-based judge | Can interpret a rubric semantically | Its interpretation can still be wrong; a missed violation can become a false pass | Try a compliant answer with a negation, a contradictory answer using expected phrases, and other counterexamples; inspect the judge’s reasons |
The goal is not to make a checker look convincing on one known failure. Try cases that could expose both false rejections and false passes, and examine the explanation alongside the result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much confidence should you place in the reported results?
The project article reports that a merged hardening revision passed 58 tests; it does not give a separate date for that test run. Its recorded evaluation on September 23, 2026, made 12 OpenAI judge requests: the judge agreed with 11 of 12 authored labels and rejected one compliant answer containing a negation. These are bounded, article-reported observations—not an independent human benchmark or evidence of production readiness. The article calls for held-out cases and further work, and does not establish the repository’s current status.
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