The outcome

Learn the interaction loop using a small task with a clear outcome.

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

    Define what debugging evidence is needed and which prompts, outputs, metadata, and identifiers must be redacted or omitted.

  2. 02

    Create a development project and environment-specific keys in a secret store.

  3. 03

    Instrument one synthetic request with a current SDK and stable trace, generation, and retrieval spans.

  4. 04

    Confirm hierarchy, redaction, asynchronous delivery, tokens, cost, latency, and failure behavior before tracing real users.

Working standard

What good use looks like.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.

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.