AI-Adjacent Issue
Category Has Too Few Issues to Be Useful
A category, diagnostic area, or intake bucket has too few Issues, examples, or checks to support useful diagnosis or routing.
What This Looks Like
A category, diagnostic area, intake bucket, or classification group exists, but it has too few Issues, examples, checks, or related references to help users diagnose real cases. The category may appear in navigation or search, but it does not provide enough coverage to be useful.
Why Users Blame AI
Users may experience the system as failing to classify or diagnose their problem. The deeper issue may be that the category itself is underbuilt: not enough Issue pages, examples, search intents, or relationship links exist to support the diagnostic surface.
What to Check First
- Whether the category has enough primary Issues
- Whether related secondary Issues help fill the category
- Whether the category has examples or guidance
- Whether common symptoms map to an existing Issue
- Whether the category is too broad for the current Issue set
- Whether missing links or rubric gaps make the category feel empty
When This Is AI-Adjacent
Use this AI-Adjacent Issue when the problem is category coverage, not a single AI failure. If a known diagnostic area has no Issue at all, use the related Workbench Issue for diagnostic coverage gap. If the rubric or relationship map is incomplete, use those related Issues.
Related Workbench Issues
Diagnostic Area Has No Coverage
A known diagnostic area, failure mode, requirement, or review dimension has no Issue, check, rubric item, or workflow coverage.
Evaluation Rubric Has Coverage Gap
An evaluation rubric, grading standard, or review checklist leaves part of the required evaluation space uncovered.
Relationship Map Has Missing Links
A map of related Issues, rules, patterns, cases, fields, tools, or workflow steps is missing links needed to navigate or reason over the structure.
Review Rubric Missing Required Criteria
A review rubric, grading rule, evaluation checklist, or classification standard lacks criteria required to make the review reliable.
Common Ways People Describe This
- category has too few issues to be useful
- AI issue category has no coverage
- diagnostic category too thin
- not enough issues in category
- category needs more examples
- ontology category coverage gap