prescription
The core pain point of difficulty in debugging LLM applications can be solved in three steps by Langfuse's observation and debugging features:
- full-link traceability: After installing the Python/JS SDK, use the @observe decorator to automatically log each call (input/output/delay) or manually insert tracepoints in the code
- Problem orientation: View invocation links through waterfall diagram in Traces interface, support filtering by session ID, exception status, quickly locate failed requests.
- context sensitive debugging: Click on a specific Trace to see the full context (including upstream function parameters), combined with Playground to instantly modify the prompt word to reproduce the problem
Especially for RAG processes, themultistage marking: Insert independent Trace at key stages such as vector retrieval/rearranging, and finally generate a visual flow chart with timeline (example below). The production environment is recommended to be deployed with Kubernetes to ensure stability under high concurrency.
This answer comes from the articleLangfuse: an open source LLM application observation and debugging platformThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of difficult debugging and lack of visual tracing of LLM applications?
