AI Training
Test, verify, keep AI under control.
an AI tool isn’t reliable because it worked once. It’s reliable because it’s measured — every release, on your cases, against your acceptance rules.
Vibes are not verification.
“looks good to me” doesn’t survive an audit. What verification means here: a check that passes or fails without a human in the loop, plus a human sign-off at the gate.
The eval harness.
Golden set
20–50 of your real cases with known-right answers.
Automated scoring
Checks run on every output: diff, claim-check, format, protected values.
Regression runs
Same set, every model update — drift shows up as a number, not a surprise.
What an eval run looks like.
Eval Run Example
# case_set: product-blurbs v7 (40 cases) case_set: product-blurbs v7 (40 cases) checks: diff · claim-check · read-grade · spelling result: 38 pass · 2 hold · 0 fail gate: publish requires owner sign-off status: 2 blurbs returned — off brand voice
Every run ends in a pass/hold/fail count and a gate decision.
What we check.
- output matches source (diff)
- no unsupported claims (claim-check)
- protected values unchanged
- readability at target grade
- spelling and format
- refusal correctness on out-of-scope input
Keep it controlled.
Baseline
Score the tool as-is, before changes.
Gate
Nothing ships below the agreed bar.
Monitor
Monthly runs, drift report.
Re-baseline
Re-run the set on every model or tool update.
Ready to measure your AI?
Start with your situation. We’ll baseline what you have before changing anything.