Vice President · TheGreyMatter.ai
In brief
A useful evaluation dataset represents the work, its exceptions, and the conditions under which an answer should be withheld.
Key takeaways
- Write acceptance criteria before collecting impressive examples.
- Keep related cases together when separating development and final evaluation.
- Report important failure categories alongside the overall acceptance rate.
An AI pilot needs a collection of cases that makes improvement visible and regression difficult to hide. A useful dataset includes ordinary work, difficult exceptions, insufficient evidence, and requests outside the permitted scope. It also records how a qualified person would judge the output. This proposed method can begin in a simple table and become more formal as the workflow and its consequences grow.
1. Write the acceptance contract
Describe one task in terms of its recipient and next decision. For an account brief, acceptance might require the correct company, reporting period, source links, unresolved questions, and separation of facts from inference. Define which mistakes cause an automatic failure. Do not let a strong writing score cancel a wrong customer identity or unauthorized disclosure. Those errors belong in separate categories with explicit consequences.
NIST's AI RMF Core calls for documenting test sets and metrics and evaluating conditions similar to deployment. Google's evaluation guidance likewise distinguishes task datasets, scoring criteria, and individual result inspection. Those are useful foundations. The specific rubric still belongs to the operating team: the people using a support answer may need a different standard from the people reviewing an internal brainstorming note.
2. Sample the work deliberately
List the task variations that matter: question type, document length, language, source age, missing fields, ambiguity, and potential consequence. Collect authorized examples across those variations. Preserve enough context to reproduce the request, but remove unnecessary identifying information through an approved process. If historical examples contain information the pilot is not allowed to process, replace them with realistic synthetic cases and label them clearly.
Include refusals and escalations as first-class cases. A request with insufficient evidence should not be scored against an invented answer. Record the expected behavior, the reason, and the safe next step. For a hypothetical renewal brief, one case could contain two contradictory contract dates. The target is to identify the conflict and request resolution, not to choose whichever date makes the narrative easier to complete.
3. Create a case record that can survive review
Give every case a stable identifier. Store the input, permitted sources, expected facts or behavior, scoring criteria, difficulty category, and provenance. Record the source version and the date a subject expert last checked the expected result. Some tasks permit several good answers, so write factual and behavioral requirements rather than demanding identical wording. Reserve exact matching for fields where exactness is actually part of the task.
Have two qualified reviewers score an initial subset independently. Compare disagreements before scoring the full collection. A dispute may reveal vague criteria, missing context, or genuinely ambiguous evidence. Repair the rubric or mark the case for specialist review. Keep examples of both passing and failing outputs with explanations. They become practical training material for future reviewers without turning one person's preferences into an invisible standard.
4. Protect the comparison
Separate cases used to improve prompts from cases reserved for the final decision. Keep near duplicates, related customer records, and versions of the same document in the same group. Otherwise the final test can resemble rehearsal. Record the model, prompt, retrieval configuration, and evaluator used for each run. Change only what the experiment intends to compare, or explain the additional changes before interpreting the result.
Automated checks can verify dates, required fields, citations, and structured output. Model-based graders can help scale review, but compare their judgments with qualified human decisions on a meaningful sample. Inspect disagreements and serious errors directly. Do not assume that an eloquent grading explanation establishes correctness. A grader can share the same blind spots as the system being evaluated, especially when both rely on incomplete evidence.
5. Keep the dataset useful after launch
Report the number of cases, acceptance rate, important failure counts, and results by relevant task category. Show the sample size next to each percentage. A small pilot can expose failures without establishing a precise production failure rate. Write down what the evaluation cannot yet support, such as performance in an untested language or on a new document type. Expansion should bring additional cases, not just more users.
Add newly observed failures to a development collection, then maintain a separate refreshed holdout for later release decisions. Review cases when policies or business definitions change. Retain the reason for each changed expected answer so a legitimate update is distinguishable from adjusting the test to flatter the system. The dataset should become a maintained description of acceptable work, with an owner, a revision history, and a clear place in release decisions.
Common questions
How many evaluation cases does an AI pilot need?
There is no universal number. Cover the important task variations and consequences first, report sample sizes, and expand the collection until it supports the decision you need to make. A small set is useful for finding failures but weak evidence for precise production rates.
Can an AI model grade the pilot?
It can assist, especially with explicit rubrics, but its decisions need calibration against qualified human review. Inspect serious failures and disagreement directly rather than relying only on aggregate automated scores.
Sources and evidence
AI assisted editorial analysis. Primary sources checked October 10, 2026. The procedures are editorial recommendations, not vendor instructions, reported customer results, or claims of personal experience. Numerical examples are hypothetical.
One useful next step: Choose one idea from this note and test it at the smallest scale that could teach you something this week.
