Question 1

When should we use LangChain instead of a simple prompt app?

Accepted Answer

Use LangChain when your use case needs multi-step logic, tool orchestration, retrieval, or reliable state handling beyond a single prompt-response flow.

Question 2

Can you build RAG with vector database support?

Accepted Answer

Yes. Experts can implement end-to-end RAG architecture including ingestion, chunking strategy, embeddings, retrieval, and ranking improvements.

Question 3

How do you monitor chain quality in production?

Accepted Answer

Teams add observability, evaluation datasets, latency tracking, and response quality scoring to continuously improve reliability.

Question 4

Can our existing app be upgraded to LangChain architecture?

Accepted Answer

Yes. Existing AI features can be gradually migrated to LangChain without rewriting the full application in one release.

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What You Can Build

FAQs

When should we use LangChain instead of a simple prompt app?

Can you build RAG with vector database support?

How do you monitor chain quality in production?

Can our existing app be upgraded to LangChain architecture?

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