LLM Evaluation and Testing for Reliable AI Apps

LLM evaluation is essential. Building with LLMs means working with complex, non-deterministic systems. Testing is critical to catch failures and risks early – and to ship fast and with confidence.

In this webinar with Evidently AI, we heard firsthand about the challenges and opportunities presented by LLM observability.

We explored:

-Real-world risks: We saw real examples of LLM failures in production environments, including hallucinations and vulnerabilities.

-Practical evaluation techniques: We shared tips for synthetic data generation, building representative test datasets, and leveraging LLM-as-a-judge methods.

-Evaluation-driven workflows: We learned how to integrate evaluation into LLM product development and monitoring processes.

-Production monitoring strategies: We discussed insights on adding model monitoring capabilities to deployed LLMs, both in the cloud and on-premises.

Links

• LLM monitoring in MLRun: https://docs.mlrun.org/en/latest/tutorials/genai-02-model-monitor-llm.html
• Monitoring in MLRun with the Evidently base class: https://docs.mlrun.org/en/latest/api/mlrun.model_monitoring/index.html#mlrun.model_monitoring.applications.e[…]identlyModelMonitoringApplicationBase