AI Evaluation & Output Review

I set up how AI output gets checked — what counts as acceptable, who reviews it, and what gets recorded when something is dropped. Most teams track which tools they adopted and never write down why they abandoned one. The second list is the more useful of the two.

When you need this

You've adopted an AI tool and nobody's quite sure if the output is actually good, or just fast. Different people on the team have different standards for what's acceptable, applied inconsistently. Something wrong has already gone out the door once, and it's made everyone nervous about trusting the tool at all. You want a real process for checking output — not a vague sense that someone should probably look at it.

How I run it

I start by making the standard explicit — what "acceptable" actually means for this specific output, written down rather than assumed. Most teams can describe a bad result immediately but have never agreed on the line in writing.

Then a review process: who checks what, how often, and what gets logged when something's rejected. The rejected cases matter more than the accepted ones — they're the record of where the tool actually breaks, and most teams throw that information away.

I set this up to run without me once it's working. The point is a system your team owns, not an ongoing dependency on someone checking by hand.

What you get

A written standard for acceptable output, specific to your use case
A review workflow — who checks, how often, what gets recorded
A log of rejected outputs, kept as a working record of where the tool fails
A handover so the process runs without ongoing involvement from me

How the work runs

Two to three weeks to set up the standard and the workflow. Works best once a tool's already in use and the gaps are visible, rather than in the abstract.