Questions
- What are the three primary limitations of raw LLMs that impact production applications, and how does LangChain address each one?
- Compare and contrast open-source and closed-source LLMs in terms of deployment options, cost considerations, and use cases. When might you choose each type?
- What is the difference between a LangChain chain and a LangGraph agent? When would you choose one over the other?
- Explain how LangChain’s modular architecture supports the rapid development of AI applications. Provide an example of how this modularity might benefit an enterprise use case.
- What are the key components of the LangChain ecosystem, and how do they work together to support the development lifecycle from building to deployment to monitoring?
- How does agentic AI differ from traditional LLM applications? Describe a business scenario where an agent would provide significant advantages over a simple chain.
- What factors should you consider when selecting an LLM provider for a production application? Name at least three considerations beyond just model performance.
- How does LangChain help address common challenges like hallucinations, context limitations, and tool integration that affect all LLM applications?
- Explain how the LangChain package structure (
langchain-core,langchain,langchain-community) affects dependency management and integration options in your applications. - What role does LangSmith play in the development lifecycle of production LangChain applications?
