LLM-as-a-judge in Langfuse

Store an API key

To use Langfuse LLM-as-a-judge, you have to bring your own LLM API keys. To do so, navigate to the settings page and insert your API key. We store them encrypted on our servers.

Create an evaluator

To set up an evaluator, we first need to configure the following:

• Select the model and its parameters.
• Select the evaluation prompt. Langfuse offers a set of managed prompts, but you can also write your own.
• We use function calling to extract the evaluation output. Specify the descriptions for the function parameters score and reasoning. This is how you can direct the LLM to score on a specific range and provide specific reasoning for the score.

We store this information in an so-called evaluator template, so you can reuse it for multiple evaluators.

Second, we need to specify on which traces Langfuse should run the evaluator.

• Specify the name of the scores which will be created as a result of the evaluation.
• Filter which newly ingested traces should be evaluated. (Coming soon: select existing traces)
• Specify how Langfuse should fill the variables in the template. Langfuse can extract data from trace, generations, spans, or event objects which belong to a trace. You can choose to take Input, Output or metadata from each of these objects. For generations, spans, or events, you also have to specify the name of the object. We will always take the latest object that matches the name.
• Reduce the sampling to not run evaluations on each trace. This helps to save LLM API cost.
• Add a delay to the evaluation execution. This is how you can ensure all data arrived at Langfuse servers before evaluation is executed.

See the progress

Once the evaluator is saved, Langfuse will start running evaluations on the traces that match the filter. You can view logs on the log page, or view each evaluator to see configuration and logs.

See scores

Upon receiving new traces, navigate to the trace detail view to see the associated scores.