note · Feb 2026
Checking an LLM's output for quality is the easy 10%. In a regulated workflow the real job is proving what went in, what came back, and that nothing left your walls — on record.
Search for an "LLM checker" and you'll mostly find tools that grade an answer — is it accurate, is it toxic, does it hallucinate. That matters, but in a regulated workflow it's the easy part. Grading the output does nothing about the data that reached the model, the fields that shouldn't have, or the absence of any record you could hand an auditor. The output was never the exposure. The pipeline was.
A checker worth running in a regulated enterprise sits in front of the model, not just after it, and does three things on every call:
privileged content — so the exposure never happens rather than getting flagged after the fact.
rules, instead of failing quietly and producing confidently wrong results.
down — logged, attributable, on record.
Grading the answer is a fourth, downstream step. Useful, but it's the 10%, not the job.
The difference that matters in regulated work is the record. When an auditor asks what personal data reached the model and whether any of it left your walls, "we think it's fine" is not an answer. A real checker produces a signed trail — what came in, what was filtered, what the model returned, where it ran — so the answer is evidence, not assurance.
That's what an Inspector is: not an output grader bolted on at the end, but a governed worker in front of the call that filters, reconstructs, detects, and keeps the record — starting read-only in Watch mode, so you can prove it before you trust it with anything.
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