Evaluation checklists
Quality gates for judging AI output before it reaches production code, documentation, examples, or customer-facing workflows.
Takeaway
Evaluate AI output with the same standards used for human work: correctness, fit, security, accessibility, tests, and maintainability.
01
Check correctness first
Confirm the output matches the current codebase, APIs, package versions, and product behavior. Plausible but outdated advice should be corrected before style polish begins.
02
Review user impact
Look for confusing labels, missing error states, accessibility regressions, privacy leaks, and misleading claims. AI output can be syntactically valid while still making the product worse.
03
Require verification evidence
Before accepting output, run the relevant checks and capture what passed. For content, verify links and claims; for code, run tests, lint, build, and focused manual flows.