Medium LangChain Interview Questions

Done with the basics? These 10 medium-level LangChain questions go deeper into real-world concepts like RAG, agents, tools, and memory management. Watch the video above for clear, detailed answers to each question.

Q1. How does RunnableParallel instantly speed up your LangChain applications?

Q2. What are Tools in LangChain, and how does the Agent know when to use them?

Q3. What exactly is a Retriever, and why is it the backbone of RAG applications?

Q4. Why do we need Output Parsers if LLMs already generate text?

Q5. How can you use Callbacks to monitor what your LLM application is actually doing?

Q6. ConversationalRetrievalChain vs RetrievalQA — which do you use for a chatbot?

Q7. What problem does a SelfQueryRetriever solve for user queries?

Q8. How does RunnablePassthrough work in LCEL, and why does RAG need it?

Q9. Can you explain the ReAct pattern used under the hood of LangChain Agents?

Q10. Why is ConversationTokenBufferMemory considered the best choice for production chatbots?

Want to go deeper into LangChain? Subscribe to AI360Xpert for weekly tutorials on AI, machine learning, and cloud computing.

Watch the full video on our YouTube channel .