The fastest way to ship a bad AI product is to demo it early. The fastest way to ship a good one is to evaluate it early.
We have watched this play out enough times — internally and with clients — that it has hardened into a rule. The first deliverable on any new AI project we run is not a prototype. It is an evaluation harness.
Why demos mislead
A demo selects for the case where the model looks good. It is a single-sample confidence interval with an audience. It tells you nothing about the distribution. A demo that works on Tuesday and fails on Wednesday has taught you nothing about Thursday.
The second failure mode is subtler. A demo anchors stakeholders on a capability the product does not reliably have. You then spend the rest of the engagement closing the gap between that anchor and reality, instead of widening the honest capability.
What an eval harness is, practically
For a language-model product, it is:
- A frozen set of 30–150 representative inputs. Not cherry-picked. Not edge cases only. The actual distribution.
- A rubric for each input — a senior human's scored answer, or a binary "ship / don't ship" call.
- A runner that executes the current prompt and model against the set on every change.
- A scoreboard visible to whoever is making build decisions, including the client.
That is the whole thing. It does not require fancy infrastructure. A CSV, a notebook, and a CI job will do.
The scorecard we use
Every AI feature we ship has to clear four gates before it reaches the interface.
- Coverage — percent of the frozen set where the output is within tolerance.
- Regression guard — no item that previously passed is now failing.
- Tail behaviour — on the hardest 10% of inputs, does it fail loudly or silently? Silent failures are disqualifying.
- Cost per passing answer — not per call. Per useful call.
A feature does not ship because it looks good in a demo. It ships because the scorecard says so.
The objection
The usual pushback is that writing evaluations "slows the team down." In our experience it does the opposite. It makes the build direction unambiguous, it collapses arguments about whether something works, and it turns prompt iteration from vibes into a gradient.
The teams that move fastest on AI products are the ones that built the bench first. Everyone else is arguing about Tuesday's demo.