What is the best method to implement automated LLM-as-a-judge metrics inside Langfuse?

All answers to this question.

Setting up automated evaluation pipelines is one of the most powerful features of this platform. You can combine custom heuristic code functions with asynchronous LLM-as-a-judge scoring frameworks to automatically scan specific trace logs. Once configured, these evaluators analyze production instances in the background, computing quality and safety scores without altering user response times. The metrics update directly onto your active analytics panels for quick review.

   Answered 2025-09-18 by Helen Mirren

Are you planning to run these evaluations via the cloud-managed infrastructure or within a self-hosted Docker deployment? The data processing latency can vary based on your backend setup.

   Answered 2025-09-25 by Walter White

• Walter, model-based evaluation processing happens completely asynchronously through standard cron workers, so backend choice won't influence user-facing application speeds. However, using a self-hosted instance backed by a ClickHouse database ensures faster query execution times when filtering millions of scored production rows.

     Commented 2025-09-29 by Raymond Reddington

You can also gather explicit user feedback by sending thumbs-up or thumbs-down clicks directly via the public SDK into specific trace IDs to balance automated scores.

   Answered 2025-10-02 by Frank Sinatra

• Combining human annotations with automated judgments gives you the best benchmark dataset. It creates a robust validation framework before pushing any prompt changes live.

     Commented 2025-10-05 by George Lucas