Langfuse provides end-to-end observability for LLM applications through structured traces, spans, and generations. These exercises cover the trace hierarchy, scoring for quality measurement, dataset-based evaluation, LangChain callback integration, and server-side prompt management.
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In Langfuse, what is the hierarchy of observability objects from broadest to most granular?
Langfuse uses a three-level hierarchy: a Trace represents one end-to-end request (e.g., a user query). Inside a trace, Spans represent timed operations (retrieval, processing). Generations are a special span type specifically for LLM calls that capture model name, input/output, token counts, and cost.
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A developer uses the Langfuse Python SDK and calls langfuse.score(). What does scoring enable?
Scores in Langfuse attach quality signals to traces. They can be added programmatically (e.g., automated evaluation metrics), via human review in the UI, or through model-based evaluation. Scores enable filtering and comparing traces by quality, which is essential for identifying regressions and evaluating prompt changes.
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What is the purpose of Langfuse Datasets?
Langfuse Datasets are curated collections of items (input + expected output) used for repeatable evaluation. You can run your LLM application against a dataset, score each result, and compare scores across different prompt versions or model configurations to measure improvement objectively.
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How does Langfuse integrate with LangChain applications?
Langfuse provides a CallbackHandler (langfuse.callback.CallbackHandler()) that implements LangChain's callback interface. Passing it to any chain, agent, or LLM call automatically captures all events — LLM calls, tool invocations, retrieval steps — as a structured trace without modifying application code.
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A team uses Langfuse's prompt management feature. What advantage does this provide over storing prompts in code?
Langfuse prompt management stores prompts server-side with version control. Applications fetch prompts via the SDK at runtime (langfuse.get_prompt('my-prompt')), meaning prompt changes take effect immediately without code redeployment. You can also link which prompt version was used to each trace for full reproducibility.