Workflow & Operations · Context Gap Analysis
Context Gap Auditor
Diagnose why an AI output is generic, wrong, or oddly confident by tracing the missing or conflicting context behind it.
ORIGINAL BY ZAIN HASEEB · 1.0.0 · DEVELOPED FROM STRAITEGY HUB AUDIENCE AND OPERATING FRAMEWORKS WITH AI-ASSISTED DRAFTING.
Copy-ready prompt
Diagnose the context failure behind this AI output. Treat the output as evidence, not as the root problem.
ORIGINAL REQUEST: {{REQUEST}}
CONTEXT PROVIDED: {{CONTEXT}}
AI OUTPUT: {{OUTPUT}}
WHAT FELT WRONG: {{FAILURE}}
WHAT A GOOD RESULT WOULD HAVE DONE: {{STANDARD}}
Classify each failure as missing fact, missing example, ambiguous objective, conflicting instruction, unstated audience, weak source, stale information, missing authority boundary, or missing quality standard. Quote the exact output signal that supports each diagnosis.
Return a ranked gap table with impact, evidence, and the smallest context repair. Rewrite only the request and context package, not the final answer. Add a short test that compares the original and repaired setup on the same input. Finish with a reusable checklist for deciding whether the next failure needs more context, a better model, a different workflow, or human judgment.