AI evaluation and testing
Traditional software either works or throws an error. AI features fail quietly — slightly worse answers, a subtly wrong tone, an edge case that now returns nonsense. Without evaluation you find out from a customer.
What this involves
An evaluation suite is a set of real inputs with known good outputs, scored automatically on every change. It turns 'this prompt feels better' into a number you can defend, and it is the single highest-leverage thing most teams are missing.
It also protects you commercially. When a provider silently updates a model, or you switch to a cheaper one, the suite tells you within minutes whether quality held.
- A test set built from your real traffic and edge cases
- Automatic scoring: exact match, semantic similarity, model-graded rubrics
- Regression runs in CI on every prompt or model change
- Benchmarking across providers on your own tasks
- Production monitoring with drift and anomaly alerts
- A dashboard your non-technical stakeholders can read
What gets measured
Accuracy is only one axis. Depending on the system we also measure refusal behaviour, citation correctness, latency at the 95th percentile, cost per request, and safety on adversarial inputs. A system that is accurate but takes eleven seconds has still failed.
Catching regressions before release
Evaluations run in your CI pipeline. A prompt change that improves one case and breaks nine others is blocked at the pull request, which is where these problems are cheap to fix.
Monitoring after launch
Real traffic drifts away from your test set over time. We sample production traffic, score it, and alert when quality moves — plus flag the new question types your users have started asking.
Frequently asked questions
How many test cases do we need?
Fifty carefully chosen cases beat a thousand random ones. Coverage of your distinct scenarios and known failure modes matters far more than volume.
Who writes the expected answers?
Your domain experts, with our help structuring it. That knowledge has to come from your side — it is usually a few focused hours, not a project.
Can you evaluate a system you did not build?
Yes, and it is a common first engagement. It gives you an objective view of where an existing feature actually stands before deciding whether to fix or replace it.
Is this worth it for a small feature?
If the feature touches customers or money, yes. If it is an internal convenience with a human reading every output, a lightweight version is enough — and we will scope it that way.
Tell us what you are building.
Send a short description of the problem and we will reply within one business day with an honest view of scope, cost and whether we are the right person for it.
Or email directly: contact@hire-ai-dev.com