The outcome
Use the platform productively without giving it unnecessary access or authority.
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.
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.
- 01
Classify the task and its data before uploading or connecting anything.
- 02
Review the current product, account, organization, retention, and training settings that apply to you.
- 03
Grant the narrowest file, repository, workspace, microphone, screen, or integration permissions needed.
- 04
Remove secrets and personal or regulated information unless your approved policy explicitly permits it.
- 05
Review the output and revoke permissions or disconnect sources that are no longer needed.
Working standard
What good use looks like.
- 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.
- Follow your organization’s approved-use policy.
- Never treat fluent output as authorization to act.
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.