Output validation and quality gates
Automated checks that validate AI outputs against defined quality criteria — format compliance, numerical consistency, completeness, and policy alignment — before the output reaches a human reviewer.
Why it matters
Human reviewers shouldn't spend their time catching formatting errors or numerical inconsistencies that a machine can detect. Quality gates filter out obviously wrong outputs before they consume reviewer attention, so humans focus on judgment calls rather than error-spotting.
Where it shows up
finance
AI-generated commentary is validated against the GL data it references — do the numbers in the narrative match the actual figures? Do all material variances have commentary? Does the format match the reporting template?
hr
Policy guidance responses are validated against the citation database — does the cited policy section exist? Is it the current version? Does the answer actually address the question that was asked?
procurement
Vendor scoring outputs are validated for completeness — are all criteria scored? Do scores fall within defined ranges? Are all mandatory evidence fields populated?
Common mistakes
- Building validation rules that are too strict — rejecting outputs that are substantially correct
- Not updating validation rules when the output format or policy changes
- Treating validation as a substitute for human review rather than a complement
- Not logging validation failures — they reveal systematic AI weaknesses
Signals that a workflow needs this pattern
- Human reviewers frequently catch the same types of mechanical errors
- Output quality varies significantly across runs or input types
- The workflow has well-defined quality criteria that can be checked programmatically
- Review bottlenecks are partly caused by reviewers spending time on detectable errors