Building reliable AI applications is challenging because it’s hard to understand how changes impact performance. Without proper evaluation, teams en

LLM-as-a-judge Evaluators for Dataset Experiments

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2024-11-19 22:30:03

Building reliable AI applications is challenging because it’s hard to understand how changes impact performance. Without proper evaluation, teams end up playing whack-a-mole with bugs and regressions. Datasets and experiments in Langfuse help transform this uncertainty into a structured engineering process.

Until now, datasets and experiments depended on custom evaluations that were added to the run via the SDKs/API. This is great if you need full flexibility or want to use your preferred evaluation library or scoring logic. There were LLM-as-a-judge evaluators, but they were limited to production runs and could not access the ground truth of your dataset (expected_output) which is necessary for a reliable offline evaluation.

Day 2 of Launch Week 2 brings managed LLM-as-a-judge evaluators to dataset experiments. Assign evaluators to your datasets and they will automatically run on new experiment runs, scoring your outputs based on your evaluation criteria.

You can run any LLM-as-a-judge prompt, Langfuse comes with templates for the following evaluation criteria: Hallucination, Helpfulness, Relevance, Toxicity, Correctness, Contextrelevance, Contextcorrectness, Conciseness

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