IVQ 51-100

1. How do you visualize multi-agent workflows using LangFuse?

2. Can LangFuse be used to monitor external API calls made by the LLM?

3. What’s the difference between input and output fields in a span?

4. How can LangFuse be used to analyze failure rates across agents?

5. Is there a way to aggregate trace data by prompt template in LangFuse?

6. How do you tag traces in LangFuse for filtering and analysis?

7. What’s the impact of LangFuse on LLM response latency?

8. Can LangFuse automatically detect anomalous behaviors or outliers?

9. How do you use LangFuse to track prompt versions over time?

10. How does LangFuse integrate with LangChain’s CallbackHandler?

11. How do you analyze prompt performance across different models in LangFuse?

12. What query capabilities does the LangFuse dashboard support?

13. Can LangFuse be used to monitor streaming LLM outputs?

14. How can LangFuse help with model comparison experiments (e.g., GPT-4 vs Claude)?

15. What types of errors or exceptions are tracked in LangFuse?

16. How do you track cost and latency metrics in LangFuse per span?

17. Can LangFuse be integrated with CI/CD pipelines for regression testing?

18. How does LangFuse handle distributed tracing across microservices?

19. What kind of dashboards or analytics panels can you create in LangFuse?

20. How does LangFuse help ensure LLM responses meet compliance or policy rules?

21. Can LangFuse correlate user feedback with specific LLM outputs?

22. How do you implement prompt evaluations using LangFuse APIs?

23. What’s the typical overhead of using LangFuse in production environments?

24. Can LangFuse support multiple projects or tenants from the same backend?

25. How do you log structured JSON input/output with LangFuse?

26. What SDKs are available for integrating LangFuse with Node.js or Go?

27. How do you use LangFuse to debug latency bottlenecks in LLM chains?

28. Can LangFuse help identify token overflow or truncation issues?

29. How does LangFuse handle role-based access control (RBAC)?

30. How do you integrate LangFuse traces with Slack or monitoring alerts?

31. What CLI tools are available for LangFuse users?

32. How can LangFuse assist with tracing hybrid pipelines (LLMs + traditional code)?

33. What steps are involved in setting up LangFuse self-hosted?

34. Does LangFuse provide an API to query traces programmatically?

35. How do you track and evaluate retries or fallback models using LangFuse?

36. Can LangFuse record intermediate tool calls in a LangChain agent?

37. How do you monitor token usage per user or session in LangFuse?

38. How does LangFuse help prevent prompt regressions?

39. What are best practices for organizing projects and tags in LangFuse?

40. How does LangFuse visualize branching logic or conditional workflows?

41. How do you implement custom scoring metrics in LangFuse?

42. Can LangFuse be integrated with prompt versioning tools like PromptLayer or Git?

43. How does LangFuse support evaluation of function-calling or tool-using agents?

44. What’s the difference between span_type="generation" and span_type="tool" in LangFuse?

45. How do you track hallucination rates over time using LangFuse?

46. Can LangFuse support multilingual LLM trace evaluation?

47. How do you secure API keys and credentials in LangFuse setups?

48. What options does LangFuse offer for long-term trace archival?

49. How do you compare trace timelines across different user personas?

50. How does LangFuse contribute to Responsible AI practices (e.g., fairness, bias detection)?