Evaluating Large Language Models With OpenEvals
Evals are systematic methods to assess the quality of LLM outputs based on application-specific criteria. They rely on high-quality data to accurately reflect real-world usage. OpenEvals and AgentEvals offer prebuilt solutions for evaluating conversational quality, writing, and structured outputs. Structured data evaluators ensure output conforms to predefined formats, while LLM-as-a-judge evaluators provide objective scoring without requiring ground truth answers. LangSmith provides tools for tracking and sharing evaluations, aiding in debugging and deploying production-grade LLM applications.
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