Tutorial shows how to build end-to-end Langfuse Observability and Evaluation Pipeline for LLMs

A hands — on tutorial demonstrates installing and configuring Langfuse, tracing calls and prompts, instrumenting a RAG pipeline, and running dataset — based evaluations using either OpenAI or a deterministic mock.

A hands — on tutorial demonstrates how to set up an end-to-end Langfuse pipeline to trace function calls, manage prompts centrally, score outputs and run dataset — based experiments — giving developers a reproducible observability and evaluation workflow for LLM applications. The guide’s walkthrough targets both development and production workloads, showing why unified tracing and evaluation matter for debugging, iteration, and model selection.

The walkthrough gives concrete setup steps and code snippets: install the langfuse and openai packages, prompt for LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY, choose a region or self-hosted URL and set LANGFUSE_HOST (examples include https://cloud.langfuse.com and https://us.cloud.langfuse.com), optionally supply an OpenAI API key, and initialize the SDK. It sets DEFAULT_MODEL to "gpt-4o-mini" when using OpenAI or to "mock-llm-v1" otherwise, imports get_client, observe, propagate_attributes and Evaluation, and runs an auth_check to confirm the connection.

The guide emphasizes that Langfuse is an open-source LLM engineering platform and that the same workflow works with either a real OpenAI backend or a deterministic mock LLM. The mock backend is seeded with a small set of facts to produce predictable replies, and the tutorial shows how to instrument a compact RAG (retrieval — augmented generation) pipeline so retrieval, prompt assembly and model outputs can be traced together in a single view.

By attaching evaluation scores and running dataset — based experiments, teams can observe, evaluate and iterate on LLM applications in a structured, production — ready manner. Running the same tracing and evaluation steps against a deterministic mock lowers the barrier to experimentation and helps developers validate prompt changes, scoring logic and experiment setups before relying on paid models.