Question 1

What is LangChain's role in RAG?

Accepted Answer

LangChain provides built-in utilities to ingest documents, split text, connect to vector stores, and chain prompts.

Question 2

Why use RAG instead of fine-tuning?

Accepted Answer

RAG is cheaper, faster, allows real-time data updates, and provides clear citations for answers without risk of hallucination.

Question 3

What is the role of the LLM in RAG?

Accepted Answer

The LLM does not search for information; it only reads the retrieved paragraphs and summarizes them into a clean answer.

Question 4

Does RAG work for private internal datasets?

Accepted Answer

Yes, RAG is widely used to build internal company chatbots that search corporate wikis and databases.

What is LangChain's role in RAG?

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