ai-evals

AI Evals

Scope

Covers

• Designing evaluation (“evals”) for LLM/AI features as an execution contract: what “good” means and how it’s measured
• Converting failures into a golden test set + error taxonomy + rubric
• Choosing a judging approach (human, LLM-as-judge, automated checks) and a repeatable harness/runbook
• Producing decision-ready results and an iteration loop (every bug becomes a new test)

When to use

• “Design evals for this LLM feature so we can ship with confidence.”
• “Create a rubric + golden set + benchmark for our AI assistant/copilot.”
• “We’re seeing flaky quality—do error analysis and turn it into a repeatable eval.”
• “Compare prompts/models safely with a clear acceptance threshold.”

When NOT to use

• You need to decide what to build (use problem-definition, building-with-llms, or ai-product-strategy).
• You’re primarily doing traditional non-LLM software testing (use your standard eng QA/unit/integration tests).
• You want model training research or infra design (this skill assumes API/model usage; delegate to ML/infra).
• You only want vendor/model selection with no defined task + data (use evaluating-new-technology first, then come back with a concrete use case).
• You want to measure overall product-market fit or retention, not AI output quality (use measuring-product-market-fit).
• You need a product requirements document that includes but goes beyond eval design (use writing-prds).

Inputs

Minimum required

• System under test (SUT): what the AI does, for whom, in what workflow (inputs → outputs)
• The decision the eval must support (ship/no-ship, compare options, regression gate)
• What “good” means: 3–10 target behaviors + top failure modes
• Constraints: privacy/compliance, safety policy, languages, cost/latency budgets, timeline

Missing-info strategy

• Ask up to 5 questions from references/INTAKE.md (3–5 at a time).
• If details remain missing, proceed with explicit assumptions and provide 2–3 viable options (judge type, scoring scheme, dataset size).
• If asked to run code or generate datasets from sensitive sources, request confirmation and apply least privilege (no secrets; redact/anonymize).