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.
4 methods
Integration testing (MCP handshakes & tool contracts)
Exercise the seams where the model meets the rest of the system — tool and function contracts, MCP handshakes, retrieval calls, API auth — to catch connectivity and contract failures end to end.
Smoke testing in CI/CD
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.
Sycophancy probing
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.
Unit testing the deterministic scaffold
Test the deterministic code around the model — prompt builders, output parsers, schema validators, tool wrappers — in isolation, with exact assertions, the way you'd test any software.