ai-evals-course/evals-skills

write-code-eval

Write code evaluators for known failure modes with objective rules.

Quelltext ansehen

Verwendung

  1. Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
  2. Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
  3. Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/ai-evals-course/evals-skills --skill write-code-eval

Vor dem Start

  • Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
  • Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

Write a code evaluator

Start with a failure mode found through error analysis. Write one check for that failure mode, much like a unit test that asserts what should hold for each trace.

  1. State the rule and identify the trace fields or reference data the check needs. If the rule requires interpretation, use write-judge-prompt.
  2. Implement the check in the project's language and eval framework. Return a result and a reason in the format that framework expects.
  3. Test known passes and failures, including borderline cases. Run the check on available traces and inspect mistakes. If the rule uses a proxy for human judgment, compare its results with human labels.

Examples

Failure modePossible check
Invalid output structureParse the output and check required fields
Missing or forbidden textMatch a string or pattern
Citation not in retrieved documentsCompare cited IDs with retrieved IDs
Bad tool callCheck arguments against the tool schema or run the call in a safe test environment
Wrong valueCompare the output with a reference value

Choose the check from the failure rule. For a failure with both objective and interpretive parts, check the objective part with code and use a judge for the rest.

Verwandte Aufgaben

aus demselben Repository

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write-judge-prompt

Design LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use write-code-eval. To validate an existing judge, use validate-evaluator.

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Run error analysis on a dataset. Build a review UI, select diverse samples, monitor annotations, and organize failure modes.

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