The outcome

Replace ad hoc prompt edits with a reproducible offline and online evaluation loop and a fast rollback path.

Langfuse is a open-source LLM engineering platform for tracing, prompt management, evaluation, datasets, and metrics. Tracing complete LLM requests, debugging agents and retrieval, managing prompt versions, running experiments and evaluations, and monitoring quality, latency, tokens, and cost. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Turn representative production successes, failures, adversarial cases, and expected outputs into a versioned dataset with access controls.

  2. 02

    Create a prompt version with explicit variables and configuration, label environments deliberately, and link the prompt to generation traces.

  3. 03

    Run an experiment against the current production baseline using deterministic code checks, human review, or calibrated model judges as appropriate.

  4. 04

    Compare quality, safety, latency, tokens, cost, and subgroup failures; inspect disagreements and do not average away critical regressions.

  5. 05

    Promote the version only after thresholds pass, monitor linked online scores, add new failures to the dataset, and roll labels back if needed.

Working standard

What good use looks like.

  • Keep a production baseline in every experiment.
  • Use code checks for deterministic requirements.
  • Promote and roll back with labels.

Observability can duplicate prompts, responses, retrieved documents, tool data, user identifiers, and secrets. Define a telemetry policy first, minimize and redact payloads, select region or self-hosting deliberately, separate environments and credentials, restrict project access, control sampling and retention, validate trace failure behavior, protect evaluation datasets, calibrate model judges with human review, and link every prompt release to reproducible experiments and rollback labels.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.