LLM Eval Harness Input Contract: Required Fields, Rejection Rules, and Handoff

The direct answer

Define the minimum input record and deterministic rejection rules before repeatable evaluation and regression testing for LLM behavior begins. The working output is A versioned input-contract table with required fields, validation rules, owners, and rejected-example fixtures.

For LLM Eval Harness, the bounded capability is repeatable evaluation and regression testing for LLM behavior. Begin only when the team can supply prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds. The documented delivery target is an evaluation suite and regression harness; anything broader requires a new scope and a new authority decision.

The copyable input contract

This input contract is for teams that need model, prompt, retrieval, or policy changes to fail in a test run before they fail for users. It begins with prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds and stays inside the documented workflow: requirement mapping, fixture curation, normal and failure cases, scoring contracts, baselines, experiment execution, review, and release gates. For LLM Eval Harness, the input contract remains reviewable because its decisions have named owners, evidence fields, and stop conditions.

Copy this LLM Eval Harness table into an intake form or machine-readable schema. Its validation column answers whether an input is usable for repeatable evaluation and regression testing for LLM behavior; its rejection column prevents an incomplete record from entering execution as though it were approved.

Example record

{
  "contract_version": "1.0",
  "request_id": "ART-18-01-EXAMPLE",
  "intended_outcome": "Define the minimum input record and deterministic rejection rules before repeatable evaluation and regression testing for LLM behavior begins.",
  "input_boundary": "prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds",
  "authority": "named owner approval required for consequences outside this artifact",
  "acceptance_status": "NOT_TESTED",
  "reopen_if": "benchmarks unrelated to the product task"
}

Contract decision

A record is admitted only when every required field is present, its source is named, and the independent reviewer can run the associated check. It is held when a missing fact could be supplied without changing scope. It is rejected when the requested effect exceeds the authority of the recorded owner or asks this product to promise an outcome outside its boundary.

Run the workflow as a sequence of decisions

The LLM Eval Harness input contract follows this working sequence: requirement mapping, fixture curation, normal and failure cases, scoring contracts, baselines, experiment execution, review, and release gates. Within this artifact, each phrase marks a state boundary for repeatable evaluation and regression testing for LLM behavior. A stage output becomes the next named input, while a failed, missing, or unavailable check keeps the dependent input contract decision closed.

Separate failure register

• FAIL-01: Benchmarks unrelated to the product task.
• FAIL-02: Expected outputs copied from one model.
• FAIL-03: Judge prompts changed without versioning.
• FAIL-04: Aggregate scores hiding high-consequence failures.
• FAIL-05: Fixtures leaking into optimization data.

The register supplies negative cases for the complete acceptance set. A reviewer determines affected checks from observed evidence; array position never asserts that one failure proves or disproves one criterion.

The producer can explain what it attempted, but the independent reviewer evaluates the evidence. If the artifact changes, its prior verdict expires. This is especially important for repeatable evaluation and regression testing for LLM behavior, where a plausible narrative can hide a stale configuration, an untested negative case, or an authority mismatch.

Failure and recovery drills

A useful LLM Eval Harness input contract explains what happens when its happy path breaks. These drills come from the accepted product truth record rather than a claim that every buyer has each failure. Use safe synthetic or authorized observations for repeatable evaluation and regression testing for LLM behavior, and keep private credentials out of every fixture.

1. Benchmarks unrelated to the product task.

Detect for LLM Eval Harness: product owner captures a direct readback or safe fixture that makes this input contract condition observable. Its record binds source, time, method, and the current ART-18-01 fingerprint.

Contain the input contract: stop only the affected LLM Eval Harness path after observing “benchmarks unrelated to the product task”. Preserve its failed material and last verified state instead of erasing evidence or blindly repeating an external effect.

Recover and prove: apply the smallest authorized LLM Eval Harness correction, then have a distinct reviewer re-evaluate the complete accepted check set. Do not select one check merely because it shares this failure's list position. If any affected input contract check cannot run, its result remains NOT_TESTED.

2. Expected outputs copied from one model.

Detect for LLM Eval Harness: evaluation designer captures a direct readback or safe fixture that makes this input contract condition observable. Its record binds source, time, method, and the current ART-18-01 fingerprint.

Contain the input contract: stop only the affected LLM Eval Harness path after observing “expected outputs copied from one model”. Preserve its failed material and last verified state instead of erasing evidence or blindly repeating an external effect.

Recover and prove: apply the smallest authorized LLM Eval Harness correction, then have a distinct reviewer re-evaluate the complete accepted check set. Do not select one check merely because it shares this failure's list position. If any affected input contract check cannot run, its result remains NOT_TESTED.

3. Judge prompts changed without versioning.

Detect for LLM Eval Harness: fixture curator captures a direct readback or safe fixture that makes this input contract condition observable. Its record binds source, time, method, and the current ART-18-01 fingerprint.

Contain the input contract: stop only the affected LLM Eval Harness path after observing “judge prompts changed without versioning”. Preserve its failed material and last verified state instead of erasing evidence or blindly repeating an external effect.

Recover and prove: apply the smallest authorized LLM Eval Harness correction, then have a distinct reviewer re-evaluate the complete accepted check set. Do not select one check merely because it shares this failure's list position. If any affected input contract check cannot run, its result remains NOT_TESTED.

4. Aggregate scores hiding high-consequence failures.

Detect for LLM Eval Harness: independent reviewer captures a direct readback or safe fixture that makes this input contract condition observable. Its record binds source, time, method, and the current ART-18-01 fingerprint.

Contain the input contract: stop only the affected LLM Eval Harness path after observing “aggregate scores hiding high-consequence failures”. Preserve its failed material and last verified state instead of erasing evidence or blindly repeating an external effect.

Recover and prove: apply the smallest authorized LLM Eval Harness correction, then have a distinct reviewer re-evaluate the complete accepted check set. Do not select one check merely because it shares this failure's list position. If any affected input contract check cannot run, its result remains NOT_TESTED.

5. Fixtures leaking into optimization data.

Detect for LLM Eval Harness: release owner captures a direct readback or safe fixture that makes this input contract condition observable. Its record binds source, time, method, and the current ART-18-01 fingerprint.

Contain the input contract: stop only the affected LLM Eval Harness path after observing “fixtures leaking into optimization data”. Preserve its failed material and last verified state instead of erasing evidence or blindly repeating an external effect.

Recover and prove: apply the smallest authorized LLM Eval Harness correction, then have a distinct reviewer re-evaluate the complete accepted check set. Do not select one check merely because it shares this failure's list position. If any affected input contract check cannot run, its result remains NOT_TESTED.

Ownership and handoff

For this LLM Eval Harness input contract, the adjudication role is independent reviewer. That role judges frozen acceptance evidence for repeatable evaluation and regression testing for LLM behavior without becoming the product owner, legal adviser, security authority, or buyer. Its handoff retains open gaps, failed evidence, changed hashes, and the next action permitted for ART-18-01.

Evidence and acceptance

Use these product-specific statements as candidate acceptance checks:

• Tests map to product requirements.
• Normal, alternate, and failure flows are represented.
• Judges and thresholds are versioned.
• High-consequence cases have explicit gates.
• Results retain model, prompt, data, and environment versions.

For every LLM Eval Harness input contract check, retain the tested object, environment or source, observation time, method, expected result, actual result, verifier identity, and artifact hash. In this ART-18-01 record, label a direct readback OBSERVED, a reproducible transformation COMPUTED, and an interpretation JUDGMENT; never merge those states into one confident claim.

The research packet observed 6 impressions across adjacent site queries such as “prompt regression testing”, “what is confirmation hacking language model evaluation”, “llm regression testing”, and “llm regression testing” for the exact Search Console property https://sincllm.com/ during 2026-06-02/2026-08-30. Those observations help locate an existing audience vocabulary. They are not search-volume estimates, do not prove demand for this exact page, and do not predict clicks or rankings.

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

Implementation checklist

• The input contract names the distinct reader job: Define the minimum input record and deterministic rejection rules before repeatable evaluation and regression testing for LLM behavior begins.
• The input boundary is explicit: prompts, representative inputs, expected outputs or judging rules, and consequence-sensitive thresholds.
• The intended deliverable is explicit: an evaluation suite and regression harness.
• Every required acceptance check has current evidence or an honest NOT_TESTED status.
• At least one negative fixture covers benchmarks unrelated to the product task.
• The independent reviewer is distinct from the artifact producer.
• Rollback or reopen conditions are written before consequential action.
• No ranking, traffic, conversion, compliance, certification, or buyer-outcome guarantee was added.

When this LLM Eval Harness input contract has a failed item, repair that named item and rerun its dependent checks. Keep the frozen threshold intact; the remaining checks cannot establish that the failed ART-18-01 condition probably holds.

Related campaign guides

These four crawlable links cover the other LLM Eval Harness operator artifacts. Each leads from the current input contract to a different reader task and promised artifact, rather than to a keyword variation of this page.

• LLM Eval Harness Evidence Packet: What to Capture Before a Decision
• LLM Eval Harness Operator Runbook: Daily Checks, Escalations, and Handoff
• LLM Eval Harness Control Dashboard: Signals, Alerts, and Review Cadence
• LLM Eval Harness Change Protocol: Versioning, Canary Tests, and Rollback