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How to version and test prompts outside your code with Langfuse

Move prompts into Langfuse Prompt Management and iterate on them in the LLM Playground.

Short answer: Run Langfuse self-hosted or on Langfuse Cloud; then create your prompts in Prompt Management and fetch them from the SDK; caching on server and client keeps latency low; when a trace shows a bad result, open it in the LLM Playground and try a prompt or model change. All steps.

Who it is forA product team where prompt changes need a code deploy and nobody knows which version is live.

The problemPrompts hard-coded in the app are slow to change and impossible to compare.

The outcomeVersioned prompts fetched at runtime, with a playground for trying changes against real traces.

What you need first

  • Docker and Docker Compose for local self-hosting, or a Langfuse Cloud account
  • The Python or JS/TS SDK, or a supported framework integration
  • For production: a VM, Kubernetes with Helm, or Terraform on AWS, Azure or GCP

Steps

  1. Run Langfuse self-hosted or on Langfuse Cloud
  2. Create your prompts in Prompt Management and fetch them from the SDK; caching on server and client keeps latency low
  3. When a trace shows a bad result, open it in the LLM Playground and try a prompt or model change
  4. Publish the better version and compare results over time

Prompt for your agent

Using langfuse/langfuse, move the prompts in <file> into Prompt Management, update my code to fetch them through the SDK, and set up the playground so I can test a new version against <example trace>.

Caveat. The README links to the docs for SDK code; the exact fetch call depends on your language.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for langfuse/langfuse and "Version and test prompts outside your code"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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