Acceptance Criteria for Repeatable Evaluation and Regression Testing for LLM Behavior: What Must Be Proven

The direct answer

Write a test for “tests map to product requirements” before execution and keep “benchmarks unrelated to the product task” as a release-blocking counterexample.

For repeatable evaluation and regression testing for LLM behavior, the relevant audience is teams that need model, prompt, retrieval, or policy changes to fail in a test run before they fail for users. The decision should cover requirement mapping, fixture curation, normal and failure cases, scoring contracts, baselines, experiment execution, review, and release gates. The supplied boundary starts with prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds and ends with an evaluation suite and regression harness, presented in reviewable form.

An eval only supports claims about its fixtures, judges, metrics, and execution conditions. Passing it cannot prove general quality or production safety.

Turn each requirement into a proof obligation

The expected deliverable is an evaluation suite and regression harness.

Use prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds as the controlled starting material.

The independent reviewer adjudicates every pass, hold, or fail verdict against these registered statements.

Cover more than the happy path

The normal flow should establish whether “tests map to product requirements” holds. An alternate flow should vary a permitted input while testing whether “normal, alternate, and failure flows are represented” holds. The failure flow should use a fixture demonstrating “judge prompts changed without versioning” and verify containment.

Add a recovery flow for “aggregate scores hiding high-consequence failures”.

Judge evidence quality and freshness

For repeatable evaluation and regression testing for LLM behavior, a result from another environment cannot prove that “judges and thresholds are versioned” holds in the buyer's environment.

Define pass, hold, and fail before execution

Keep sign-off independent

The implementer may produce artifacts, but the independent reviewer should judge whether “results retain model, prompt, data, and environment versions” holds against criteria written before the result was seen.

Record the business decision of the product owner, the technical evidence reviewed by the fixture curator, the acceptance verdict recorded by the independent reviewer, and residual risk accepted by the release owner.

A screenshot or self-score cannot prove that “results retain model, prompt, data, and environment versions” holds under the failure condition “fixtures leaking into optimization data”.

Reopen criteria when the system changes

Changes to requirement mapping, fixture curation, normal and failure cases, scoring contracts, baselines, experiment execution, review, and release gates can invalidate a test even when the requirement text stays the same.

How the sources bound the acceptance criteria decision

For repeatable evaluation and regression testing for LLM behavior, the live catalog limits the offer to two elements. The supplied boundary is prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds. The catalog names the deliverable as an evaluation suite and regression harness. It cannot establish whether “tests map to product requirements” holds in the buyer's environment.

Connect those narrow roles to a local fixture for “expected outputs copied from one model” rather than treating citation status as a pass.

For repeatable evaluation and regression testing for LLM behavior, limit the conclusion to the documented workflow and let the evaluation designer retain the current source-to-claim map. Keep the source decision provisional while the failure case “aggregate scores hiding high-consequence failures” remains unresolved.

Product-specific acceptance criteria review drills

These drills connect repeatable evaluation and regression testing for LLM behavior to concrete inputs, failures, acceptance statements, and owners. For repeatable evaluation and regression testing for LLM behavior, the drills map each criterion to a reviewable verdict.

Acceptance for repeatable evaluation and regression testing for LLM behavior is judged against a boundary record covering prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds, never live protected material. The product owner requires synthetic, non-secret cases; messages, writes, state changes, and all other external effects stay inside the fixture throughout and after each case.

Requirement trace

Treat “benchmarks unrelated to the product task” as a reason to run the requirement trace review, not as a reason to guess. The product owner traces the condition through requirement mapping, fixture curation, normal and failure cases, scoring contracts, baselines, experiment execution, review, and release gates.

Retain a boundary record covering prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds, the observed output, and the test for “normal, alternate, and failure flows are represented”. This makes the decision reproducible.

When evidence supports “normal, alternate, and failure flows are represented”, the independent reviewer can close the requirement trace review. Contradictory evidence fails the drill; stale evidence keeps it open. In the requirement trace review, evidence for “normal, alternate, and failure flows are represented” maps support to pass, contradiction to fail, and unresolved to hold.

The judgment expires after a material change to requirement mapping, fixture curation, normal and failure cases, scoring contracts, baselines, experiment execution, review, and release gates or to the evidence used by the independent reviewer.

Normal-flow result

Make “expected outputs copied from one model” the negative case for the normal-flow result review. The evaluation designer follows the case through requirement mapping, fixture curation, normal and failure cases, scoring contracts, baselines, experiment execution, review, and release gates until the first unsupported transition.

Document which element of the boundary covering prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds is relevant to “high-consequence cases have explicit gates”, then ask the fixture curator to label the observation as supporting, contradictory, or incomplete without recording the acceptance verdict.

The independent reviewer closes the normal-flow result review only when the record resolves “high-consequence cases have explicit gates”; otherwise the listed deliverable remains provisional. In the normal-flow result review, evidence for “high-consequence cases have explicit gates” maps support to pass, contradiction to fail, and unresolved to hold.

Reopen the case if the operating response to “expected outputs copied from one model” changes, even when the title and stated requirement remain the same.

Alternate-flow result

Use the alternate-flow result review to examine what follows from the failure case “judge prompts changed without versioning”. Before intervention, the fixture curator retains the observable handoff.

Pair a scope record covering prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds with a direct observation of whether “tests map to product requirements” holds. The release owner retains the source and result together.

When evidence supports the finding “tests map to product requirements”, the independent reviewer advances the review; a gap makes the independent reviewer keep an evaluation suite and regression harness at hold. In the alternate-flow result review, evidence for “tests map to product requirements” maps support to pass, contradiction to fail, and unresolved to hold.

The next review is triggered when evidence for “tests map to product requirements” becomes stale or the fixture curator loses authority over the case.

Failure-flow result

Build the failure-flow result review around a case involving “aggregate scores hiding high-consequence failures”. The release owner checks which observed state in requirement mapping, fixture curation, normal and failure cases, scoring contracts, baselines, experiment execution, review, and release gates can support the next step.

Freeze a description of the boundary covering prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds before testing whether “judges and thresholds are versioned” holds. The release owner links each observation to that frozen description.

The independent reviewer records whether the criterion “judges and thresholds are versioned” is supported, contradicted, or unresolved. It grants no broader status to an evaluation suite and regression harness. In the failure-flow result review, evidence for “judges and thresholds are versioned” maps support to pass, contradiction to fail, and unresolved to hold.

The result expires when the workflow boundary for requirement mapping, fixture curation, normal and failure cases, scoring contracts, baselines, experiment execution, review, and release gates no longer follows the tested path or when evidence for “judges and thresholds are versioned” cannot be replayed.

Independent verdict

Begin with the adverse condition “fixtures leaking into optimization data”. During the acceptance criteria review, the release owner locates its first observable effect inside requirement mapping, fixture curation, normal and failure cases, scoring contracts, baselines, experiment execution, review, and release gates.

Link the independent verdict review to a scope record covering prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds and the proof target “results retain model, prompt, data, and environment versions”. The retained record identifies both versions.

The independent reviewer resolves the independent verdict review by comparing the observed result with “results retain model, prompt, data, and environment versions”. Missing proof makes the independent reviewer block acceptance of an evaluation suite and regression harness. In the independent verdict review, evidence for “results retain model, prompt, data, and environment versions” maps support to pass, contradiction to fail, and unresolved to hold.

Create a fresh record when the failure case “fixtures leaking into optimization data” appears beyond the tested boundary or when the prior evidence becomes stale.

Evidence expiry

For the evidence expiry review, freeze a case involving “benchmarks unrelated to the product task”. The product owner identifies the affected handoff before any repair begins.

Anchor the drill in a current scope record covering prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds and ask for evidence that “normal, alternate, and failure flows are represented” holds. A missing artifact leaves the evidence expiry review on hold.

Let the independent reviewer decide whether the criterion “normal, alternate, and failure flows are represented” passed under the recorded conditions. That verdict controls only this review slice. In the evidence expiry review, evidence for “normal, alternate, and failure flows are represented” maps support to pass, contradiction to fail, and unresolved to hold.

Do not reuse the disposition when the failure case “benchmarks unrelated to the product task” occurs under conditions outside the recorded input and authority boundary.

Frequently asked question

What acceptance criteria should I use for LLM Eval Harness?

Require observable evidence that tests map to product requirements and include “benchmarks unrelated to the product task” as a negative case. The independent reviewer should record pass, hold, or fail before expansion.

A product bridge, with a boundary

The LLM Eval Harness is the relevant sincLLM offer for this narrow problem. The frozen live catalog describes its required boundary as prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds and its deliverable as an evaluation suite and regression harness. Delivery under the catalog scope cannot by itself prove buyer fit, legal compliance, system safety, technical adequacy, or a business outcome.