Rippling's AI evaluation pipeline
Inside @Rippling’s eval pipeline: ✅ Offline evals: Pre-recorded mocks + fixtures that run locally on...
Rippling's approach offers a practical model for companies building reliable AI systems without external dependencies.
Rippling implements offline evals using pre-recorded mocks and fixtures that run locally on every commit. The company conducts post-merge integration evals with 300-400 queries against a full sandbox. Deploy-blocking evals test ~10 critical scenarios against real systems. Continuous evals run multiple times daily against production data.
Inside @Rippling’s eval pipeline: ✅ Offline evals: Pre-recorded mocks + fixtures that run locally on...
Inside @Rippling ’s eval pipeline: ✅ Offline evals: Pre-recorded mocks + fixtures that run locally on every commit without external dependencies. ✅ Post-merge integration evals (online): 300-400 queries against a full Rippling sandbox to validate system health before deployment. ✅ Deploy-blocking evals (online): ~10 critical scenarios against real systems that gate every deployment. ✅ Continuous evals (online): Scheduled runs against prod data, multiple times daily, monitoring live system health. 💬 3 🔄 1 ❤️ 5 👀 1512 📊 3 ⚡
