Evals Resources

Choosing and checking a resource

Choose an evaluation by the property you need to inspect. Tool decision reviews compare a recorded action with an allowed registry. RAG reviews separate retrieved evidence from answer claims. Refactoring reviews compare observable behavior, while SQL exercises compare query results against a defined dataset. These are different jobs; a score from one does not establish the others.

Open the listing and inspect what it actually includes. A manual rubric or a few example cases are not an installed runner, a large benchmark dataset or a recorded model score. Compatibility labels describe intended environments. Any framework, model judge, credentials, harness and dependency version must be configured and verified separately.

Start with a small synthetic exercise. In the RAG museum example, correct retrieval of the opening time and admission price does not prevent the answer from inventing a different price. Record the source passages, ordered hits and individual answer claims before calculating a metric. Mark unsupported claims and missing evidence explicitly rather than using a single impressive percentage.

Keep the question, reference answer, fixture data, scorer definition and software versions with each result. A changed test or registry can invalidate old evidence. Retain failed and inconclusive cases. Toy examples clarify calculations; they cannot estimate reliability across your real users or establish a universal release threshold.

Review source attribution and redistribution terms before sharing templates or datasets. The bundled AI-generated examples supplied by SimplyUtils have unconfirmed redistribution terms and are not claimed to be expert-authored benchmarks. Another model provider may process evaluation prompts and charge for requests. Use permitted synthetic data and inspect those settings before running a judge.

If a download mentions a nonexistent script or an unavailable package, report the listing URL and exact missing requirement through the contact page. Do not invent runner results or silently substitute a different test. Directory search uses titles, descriptions and tags; a zero result may reflect a filter or wording rather than a supported capability.

Write the release decision before choosing an evaluation template. A tool-registry exercise asks whether an action and its arguments are allowed; a retrieval exercise asks whether required passages were found and whether answer claims have support; a component scorecard asks whether defined interaction states behave as intended. These measurements can reveal different defects in the same assistant workflow. Treating them as interchangeable percentages hides which observation should lead to a repair.

Use a small fictional task to identify the required evidence. For a garden-museum answer, supply passages stating an opening time and an admission price. A retrieval-only measurement can show that both passages were returned while the answer still states the wrong price. Choose a grounding review for that answer defect, not a score based solely on retrieval hits. Keep the actual answer sentence and supporting passage together so the disagreement is inspectable.

Distinguish a reference set from a sampled workload. Four deliberately chosen cases can expose an absent tool, a rounding boundary or a missing tenant condition. They do not tell you how often those errors occur among real users. Label the local fixture score with its denominator and purpose. If you need an operating estimate, design a permitted representative collection, define the judging process and retain failures and unknowns rather than presenting a tiny demonstration as a production benchmark.

Inspect the scorer definition before interpreting a number. Counting correct decisions across all tasks differs from counting unsupported calls among attempted calls. Averaging form-state points can hide a false completion state that should block a release. A metric without its denominator, reference contract and case-level record is difficult to reproduce. Prefer a report that shows each failed requirement and its evidence over one that supplies only a badge or a confidence-looking summary.

Keep fixture inputs separate from judge prompts and generated results. For the SQL tenant example, the rows and expected total define the task; the proposed query is the candidate; actual query results would be an observation from a separately configured database. A model judging the query text is another observation with its own limits. Do not mix those records or imply that a database executed merely because a judge predicts the right total.

Version the exercise when its contract changes. Adding an allowed deletion tool changes the registry reference. Adding an accessibility-label requirement changes the component rubric denominator. Changing a museum price changes the supported answer. Preserve the older results under their original definitions and rerun the affected cases. Editing the old score to fit a new reference makes comparison misleading and can hide the reason a previously accepted output now needs review.

Check execution requirements and data processing before adapting a listing. A readable rubric does not include every runner, package, model account or dataset it mentions. Identify the actual implementation and provide only inputs you are allowed to share. Synthetic garden facts, order rows and component responses can explain the test without customer records. If an evaluation needs an external judge or provider, record that connection and its operating limits separately from the local arithmetic.

Use the directory as a selection and reading surface. Filters describe resource metadata, not evidence that a template ran successfully or that its tags certify compatibility. Open the full listing, inspect attribution and unconfirmed terms, and compare its actual examples with your decision. For a problem report, include the listing URL, precise missing requirement or disputed score definition and a redacted failing case. That is more actionable than asking an editor to reproduce an unexplained quality percentage.

• Agent Tool Decision Review - AI-generated manual evaluation template with three synthetic tool decisions and a small registry example. No fifty-case dataset, automated harness or measured agent score is supplied.
• RAG Grounding Review Exercise - AI-generated manual RAG evaluation template with a tested synthetic grounding and ranked-retrieval exercise. It supplies no model judge, universal threshold or Ragas integration.
• React Component Review Scorecard - AI-generated manual component review rubric with a tested native Save button example, keyboard checks and explicit untested criteria. It is not a WCAG certification or automated scorer.
• Refactoring Safety Review Exercise - AI-generated manual refactoring review template with tested mutation and falsy-value counterexamples. Passing tests or type checks is not automatically a perfect equivalence score.
• SQL Generation Review Exercise - AI-generated manual SQL review template with a tested SQLite date-range and grouping exercise. It supplies no fifty-task benchmark, query firewall or production database connection.