A convincing demo is the weakest possible evidence that an AI feature will work. Reliability is not whether a model can produce an impressive answer once; it is how often it produces an acceptable answer on your real task, with your data, over time - and what happens on the cases where it does not.
This note describes how we approach evaluating whether a model is good enough to ship, framed as an engineering question rather than a leap of faith.
Define "good enough" before you measure
Reliability only means something against a target. Before evaluating anything, we agree what an acceptable outcome is for the specific task, and what the cost of each kind of error is - because a wrong answer that a person catches in review is very different from a wrong answer that acts automatically. The bar is set by the consequence, not by a generic benchmark.
Measure on your task, not a leaderboard
Public benchmarks tell you little about performance on your data and your edge cases. We build a representative evaluation set from real (or realistic) examples of the actual work, including the awkward and ambiguous cases, and measure against that. A model that tops a leaderboard can still fail on the specific documents, phrasing or formats your business deals with every day.
- Use real, representative examples - including edge cases and failure-prone inputs.
- Measure the metrics that map to consequences, not just an aggregate score.
- Separate the cases the model must get right from the ones a person will review.
- Re-measure over time - performance can drift as inputs and models change.
Design for the errors you will still have
No model is perfect, so reliability engineering is partly about what happens when it is wrong. We validate outputs against schemas, add fallbacks for low-confidence cases, and route uncertain or consequential results to a person. The aim is a system whose failure modes are visible and safe, not one that hides its mistakes behind a confident tone.
Framed this way, "is the model good enough?" becomes answerable: good enough means it clears the agreed bar on a representative evaluation of your task, and the residual errors are caught by design. That is a decision you can defend - not a hope.
