Here is how to trace and evaluate your LLM app with Langfuse.

Step 1: Set up your Langfuse project. Create a project and grab your API keys. Langfuse is open source, so you can self-host or use the cloud version depending on your needs.

Step 2: Instrument your application. Add the Langfuse SDK to your app to start logging requests, prompts, model calls, and responses. A few lines of code give you end-to-end traces for every user request.

Step 3: Inspect traces. Open the trace view to see each step of an LLM call: the prompt sent, the response received, token usage, latency, and cost. This makes debugging a bad answer much faster than guessing.

Step 4: Monitor production metrics. Use the dashboard to track error rates, latency, and spend over time. Set up alerts so you notice regressions before they noticeably affect users.

Step 5: Compare prompt versions. When you change a prompt or model, use Langfuse to compare behavior side by side. This helps you confirm an update improves quality rather than degrading it.

Step 6: Run evaluations. Create evaluation datasets and run automated evals to catch regressions in prompt quality and answer correctness. Continuous evaluation keeps your model performing well as you iterate.

Observability is essential for production AI. With Langfuse, you can debug issues quickly, control costs, and continuously improve the quality of your LLM application with confidence.