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🚀 Netlify Preview Deployed! Preview URL: https://pr-484--test-mlflow-website.netlify.app DetailsPR: #484 This preview will be updated automatically on new commits. |
B-Step62
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Feb 24, 2026
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LGTM once the video is replaced to the correct one!
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| <video width="100%" controls autoPlay loop muted> | ||
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| src={require("./automatic-evaluation-ui-setup.mp4").default} |
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It seems this video supposed to be a different one.
| image: img/blog/mlflow-automatic-evaluation-thumbnail.png | ||
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| We're excited to introduce **Automatic Evaluation**, a new capability in MLflow that runs LLM judges on your agent traces and conversations as they're logged—no code required. |
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nit: can we replace em dashes?:)
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| Automatic evaluation runs [**LLM judges**](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/#llms-as-judges) on your traces and conversations as they're logged to MLflow. You configure judges once, and they run automatically on new traces as they arrive. Judges run asynchronously on the MLflow server, so your application's performance is unaffected. | ||
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| An LLM judge is a language model that evaluates the outputs of an agent or LLM application against specific criteria—safety, groundedness, tool usage, user satisfaction, and more. MLflow provides a large ecosystem of [**built-in judges**](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/llm-judge/predefined/) for common evaluation criteria, plus integrations with [DeepEval](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/third-party#deepeval), [RAGAS](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/third-party#ragas), and [Phoenix Evals](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/third-party#arize-phoenix-evals). The [`make_judge()`](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/llm-judge/custom-judges/) API also enables you to create custom judges from natural language criteria. |
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| An LLM judge is a language model that evaluates the outputs of an agent or LLM application against specific criteria—safety, groundedness, tool usage, user satisfaction, and more. MLflow provides a large ecosystem of [**built-in judges**](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/llm-judge/predefined/) for common evaluation criteria, plus integrations with [DeepEval](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/third-party#deepeval), [RAGAS](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/third-party#ragas), and [Phoenix Evals](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/third-party#arize-phoenix-evals). The [`make_judge()`](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/llm-judge/custom-judges/) API also enables you to create custom judges from natural language criteria. | |
| An LLM judge is a pair of a language model and a tailored prompt that evaluates the outputs of an agent or LLM application against specific criteria—safety, groundedness, tool usage, user satisfaction, and more. MLflow provides a large ecosystem of [**built-in judges**](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/llm-judge/predefined/) for common evaluation criteria, plus integrations with [DeepEval](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/third-party#deepeval), [RAGAS](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/third-party#ragas), and [Phoenix Evals](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/third-party#arize-phoenix-evals). The [`make_judge()`](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/llm-judge/custom-judges/) API also enables you to create custom judges from natural language criteria. |
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