Question 1

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 2

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 3

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.

Question 4

How do you evaluate RAG quality?

Accepted Answer

By measuring Retrieval Recall (did you get the right context?) and Generation Faithfulness (did the LLM answer match the context?).

Why use RAG instead of fine-tuning?

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