IVQ 101-150

1. How do you implement agent handoff between two agents in LangChain?

2. How can LangChain be used for form-filling or structured data extraction tasks?

3. What’s the difference between Runnable and Chain in LangChain?

4. How do you implement nested chains in a complex LangChain workflow?

5. How do you pass dynamic runtime parameters (e.g., API keys) into a LangChain pipeline?

6. How do you simulate user inputs or mock components for testing in LangChain?

7. What role does RunnableWithFallbacks play in increasing reliability?

8. Can LangChain chains be paused and resumed later in a session?

9. How do you handle tool conflicts when multiple tools are relevant to a prompt?

10. How do you use LangChain with authentication-protected APIs or systems?

11. What’s the recommended way to persist memory across user sessions?

12. How do you segment memory for multi-user applications in LangChain?

13. Can LangChain be used for document classification or tagging workflows?

14. How do you chain together LLM calls and deterministic logic (e.g., Python functions)?

15. What are best practices for designing production-safe agents in LangChain?

16. How do you detect and handle hallucinations in LangChain pipelines?

17. What is the best way to unit test and validate LangChain agents?

18. How do you use LangChain to trace data lineage in multi-step workflows?

19. How can you monitor latency and cost metrics in LangChain apps?

20. How do you visualize a LangChain pipeline end-to-end, including memory, tools, and outputs?

21. How do you build a LangChain chatbot with real-time web search integration?

22. What strategies help reduce token usage in LangChain applications?

23. How do you use LangChain to dynamically switch between LLM providers?

24. How can LangChain be combined with function calling to complete API workflows?

25. How do you use PromptTemplate.from_template() effectively?

26. What’s the difference between RunnableLambda and RunnableFunction?

27. How do you schedule periodic LangChain tasks (e.g., daily summary generation)?

28. How do you implement retry and timeout mechanisms at the tool level?

29. Can LangChain be used to generate and validate SQL queries from natural language?

30. How do you log all intermediate steps and tool outputs in an agent run?

31. How can you restrict agent tools to specific user roles or access levels?

32. What’s the best way to evaluate long-context summarization pipelines in LangChain?

33. How does LangChain support tool observation and adaptive tool selection?

34. How do you handle complex branching logic in LangChain without LangGraph?

35. How do you build a LangChain pipeline that outputs both structured and unstructured results?

36. How can LangChain be used to chain together LLMs with different capabilities (e.g., GPT-4 and Claude)?

37. How do you apply input sanitization and validation across multiple LangChain steps?

38. How can you benchmark multiple retrieval strategies within the same LangChain app?

39. What are LangChain’s best practices for load testing and performance benchmarking?

40. How do you implement multilingual support in LangChain pipelines?

41. How do you log user interaction history and tie it to LangChain traces?

42. Can LangChain support hybrid agents combining symbolic logic and LLM reasoning?

43. How do you implement user-specific prompt customization in LangChain apps?

44. How can LangChain be used for dynamic knowledge graph construction?

45. What’s the role of the RunnableSerializable interface in pipeline modularity?

46. How do you version control prompt templates in a collaborative LangChain project?

47. How can LangChain be used to orchestrate document ingestion and Q&A in one flow?

48. What are the security considerations when exposing LangChain agents via API?

49. How do you simulate different user intents for automated LangChain testing?

50. How do you extend LangChain components to support new model APIs or providers?