# LLM-based test generation

Meta has moved its large-language-model-generated just-in-time tests (JiTTests) from research prototype to production infrastructure, generating tests designed to fail so that a code change's behavioral difference surfaces before it lands. Two tandem workflows, a dodgy-diff path and an intent-aware path, produce candidate tests, which a multi-model LLM-as-Judge ensemble and a rule-based assessor then rank, with engineers intervening at the aggregate level. In five months 589 engineers engaged and reported a 60% true-positive rate; in the first half of 2026 the system caught 74 serious privacy bugs at the diff stage, alongside a 20% drop in privacy severe bugs over that period.

Meta has moved its large-language-model-generated just-in-time tests (JiTTests) from research prototype to production infrastructure, generating tests designed to fail so that a code change's behavioral difference surfaces before it lands. Two tandem workflows, a dodgy-diff path and an intent-aware path, produce candidate tests, which a multi-model LLM-as-Judge ensemble and a rule-based assessor then rank, with engineers intervening at the aggregate level. In five months 589 engineers engaged and reported a 60% true-positive rate; in the first half of 2026 the system caught 74 serious privacy bugs at the diff stage, alongside a 20% drop in privacy severe bugs over that period.