Qlarify Labs
Catalog
How to find the limits of AI systems. Each entry is a repeatable testing technique — the durable knowledge, independent of any one model or version.
Qlarify Labs
How to find the limits of AI systems. Each entry is a repeatable testing technique — the durable knowledge, independent of any one model or version.
18 methods
Serve two variants — prompts, models, or settings — to comparable slices of real traffic and let live outcomes decide which behaves better.
Deliberately craft inputs designed to elicit failure — confusion, unsafe output, or broken constraints — to map the model's weak boundaries.
Score the model against a fixed, known-answer dataset so performance becomes a number you can track across versions and compare across models.
Measure outcome disparities across a population of demographically varied inputs, reporting fairness as statistics rather than anecdotes.
Push inputs to limits — very long contexts, token boundaries, empty/extreme values — where behavior tends to degrade sharply.
Test whether a model's stated reasoning actually determines its answer, or is a post-hoc rationalization.
Hold a prompt fixed while swapping a protected attribute (name, gender, ethnicity) — the output should not change. When it does, you've measured bias.
Run the same input across models or versions and treat divergence as a signal worth investigating.
Sample the model many times and test the distribution of its outputs — not any single answer — for drift, miscalibration, or instability.
Check generated claims against a trusted ground-truth source to catch hallucinations and fabricated citations.
Feed anomalous tokens, rare unicode, homoglyphs and malformed encodings to trigger out-of-distribution behavior.
Check that the model's outputs obey the rules of logic — valid inference, transitivity, symmetry, no self-contradiction — across related questions.
Test without an oracle by checking relations between the outputs of related inputs, instead of judging any single output in isolation.
Use a strong model as an approximate oracle — grading, comparing, or fact-checking another model's output where no cheap ground-truth label exists.
Specify invariants the output must always satisfy, then generate many inputs automatically and check the invariant on each.
Ask the same question multiple times (or multiple ways) and measure how often the answers agree.
A fast, shallow pass on every build — a handful of canonical prompts and health checks — whose only job is to fail loudly on gross breakage before anything deeper runs.
Deliberately probe whether a model abandons a correct answer or endorses a false one when the user pushes back, to catch approval-seeking behavior that plain accuracy evals miss.