Application evals — testing AI systems where the model is one component of a larger piece of software. Each entry is a repeatable technique for catching regressions, measuring accuracy, and finding the failures that create risk for the application and the people who depend on it. Not the foundational benchmarks that rank base models on leaderboards.
11 methods
Serve two variants — prompts, models, or settings — to comparable slices of real traffic and let live outcomes decide which behaves better.
Check that the system meets the transparency duties the law places on its provider and deployer — people told they are talking to an AI, generated content marked as such — in every channel, at the latest at the first interaction, and every time rather than most of the time.
Compare the AI feature with the process it replaces — the search box, the rules engine, the form, the person — on the same work, so 'it works' is measured against what users had before rather than against nothing.
Check that the system speaks the way the organization wants to be seen — in voice, in role, and above all in what it promises — and never commits the organization to a policy, price or exception that doesn't exist.
Route a small slice of real traffic to a new model or prompt first, watch it closely, and widen or roll back based on what the canary shows.
Deliberately inject failures — tool timeouts, malformed tool responses, truncated context, adversarial inputs — to test whether the system degrades gracefully and recovers.
Re-run a fixed suite against each release and over time, watching for the quiet regressions and capability decay that a one-off evaluation can't see.
Test the human in the loop, not just the model — seed known errors into the system's output and measure whether the people overseeing it catch them, understand what it is doing, and can actually override or stop it.
Find out what people actually use the system for, beyond what it was built for, and test how it handles those uses — serve them well, decline them clearly, or redirect — rather than improvising.
Instrument the whole request — prompt, retrieval, model call, tool calls, tokens, latency, outcome — so real production behaviour becomes evidence you can query, sample, and score.
Push volume, length, and concurrency past what the system enforces — unbounded prompts, recursive agent loops, or scripted request floods — to find where cost or capacity, not correctness, breaks first.
