LLM-as-a-Judge: Example of How To Build a Custom Evaluator Using a Benchmark Dataset

Arize-Phoenix provides pre-built evaluators for common scenarios, but for specialized domains like medicine, finance, and agriculture, creating a custom evaluator is often necessary to ensure high accuracy. New tutorials demonstrate how to build a custom evaluator in both Arize AX and Phoenix, starting with the creation of a benchmark dataset by annotating realistic examples and defining clear label definitions. By running experiments and iterating on the evaluation template where results disagree, users can develop a judge that aligns with their application's quality definitions. This iterative process enhances the evaluator's performance, making it adaptable to various workloads, such as validating summaries or checking citation correctness. These processes can be executed using notebooks available in both Phoenix and Arize AX platforms, with tools for configuring tracing, generating traces, and refining templates for optimal evaluator performance.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.