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A completeness gate can only check an answer against the requirements it is given. If the model being evaluated also creates that list, an important omission may vanish from both the answer and the score. Define the required set separately, declare its scope, and review any model-drafted criteria against an independent reference before using them to accept or reject an answer.
What a completeness score actually tells you
A completeness score answers a conditional question: given a particular set of required items, how many did the response satisfy? It does not establish that the set includes everything the task requires.
Amazon Web Services defines its Bedrock judge-based metric this way: “Completeness (Builtin.Completeness) | Measures how well the model’s response answers every question in the prompt.” The documentation also notes that the evaluator considers a supplied reference response when one is included. That is a useful description of the metric’s scope: the score concerns the response relative to the prompt and any reference provided, not whether those inputs captured every relevant requirement. Amazon Bedrock documentation
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFuture AGI similarly describes completeness as checking whether a response covers what the input asks and flagging gaps. This is a vendor’s description of a commercial evaluation feature, not independent evidence that a completeness score can validate its own criteria. Future AGI’s completeness documentation
Keep the required set independent of the answer being scored
Maintain the criteria as an explicit evaluation input outside the model output under review. A model may help draft or expand a checklist, but that draft should not become the final gate simply because the model produced it. Compare it with human-authored criteria or another independent reference, investigate disagreements, and resolve them before scoring.
Research on generated checklists offers one example of this validation approach: TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation describes comparing instruction-specific generated checklists with human-written checklists. That comparison can help reveal missing or mismatched criteria; it does not guarantee that either list is universally complete. The source is a ResearchGate preprint listing, so it should not be treated as proof of a universal result. TICKing All the Boxes listing
Make scope and ownership visible
A checklist is meaningful only within a declared scope. Record what task, output, audience, and decision it applies to, and who owns the criteria. Human-authored criteria are not automatically complete either: they need an explicit scope and review. The goal is not to claim a perfect checklist, but to make its assumptions inspectable and its omissions easier to find.
Established reporting guidance illustrates why task fit matters. TRIPOD-LLM includes 19 main items and 50 subitems; some apply broadly, while others are specific to research designs or LLM task categories. Those counts describe the reporting guideline, not the effectiveness of completeness gates. TRIPOD-LLM reporting guideline
The Open Source Initiative’s machine-learning checklist is likewise tied to a declared framework, with required and optional components. It is an example of scoped guidance, not a universal requirements list for every AI system. Open Source Initiative checklist
Version the checklist and the evaluation setup
Give the required set a stable version and preserve the origin and rationale for each criterion. When criteria change, record what changed and which evaluations used which version. Otherwise, a score can shift because the checklist changed rather than because the system improved or declined.
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Record the evaluated model, prompt or harness, evaluation content, references, and validity checks alongside the result. OpenAI’s guidance on third-party evaluations stresses the value of a standardized setup so readers can distinguish changes in systems from changes in measurement. It also recommends reporting evaluation content, the tested system, budget, elicitation methods, and checks for threats such as reward hacking or contamination. OpenAI’s evaluation playbook
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A practical workflow for a trustworthy gate
- Define the decision and scope. State what output the gate evaluates and what “complete enough” means for the intended use.
- Establish the required set independently. Build criteria from task requirements, applicable guidance, and accountable human review—not solely from the model being evaluated.
- Use model suggestions as proposals. If a model drafts criteria, compare them with the established set or another independent reference and adjudicate omissions and disagreements before scoring.
- Version and trace the criteria. Keep the checklist version, scope, criterion origins, and change history with the evaluation record.
- Capture the full setup and inspect validity. Record the model, prompt or harness, references, evaluation content, and relevant checks so the score can be interpreted and reproduced.
What a gate can and cannot establish
A well-designed completeness gate can consistently test whether an answer covers a reviewed set of requirements. It cannot, by its score alone, prove that the set itself is exhaustive. The available sources support this design principle and describe checklist comparison and reporting practices; they do not establish a failure rate for gates whose criteria are generated by the evaluated model, or prove that one method of building a checklist is optimal.
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