In simple terms, what is retrieval augmented generation, and why would you use it instead of just asking a language model directly?

What the interviewer is looking for

• This question tests retrieval augmented generation. Discusses chunking, embeddings, hybrid search, reranking and metadata filters, and measures retrieval quality separately from answers.
• Strong answers: Fetches relevant documents and passes them as context so answers are grounded, current, citable and less likely to be made up.
• Likely follow-up: What are the main ways a retrieval system can still give a wrong answer?

Another way it may be asked: A company wants a chatbot that answers questions about its own policies. Why not just ask the model, and what does retrieval add?

A structure to follow: Answer, explain, example, limits

1. Step 1

   Answer

   Give your direct answer in the first sentence so the interviewer knows where you are going.

2. Step 2

   Explain

   Say how it works or why you would do it that way, in plain words before jargon.

3. Step 3

   Example

   Anchor it in something you have actually done or seen, with the detail that shows hands-on experience.

4. Step 4

   Limits

   Say when your approach would not be the right one. It shows judgement.

This is guidance on shape, not a script. Use your own real experience and facts; never invent details to fill a gap. See the STAR guide and answer frameworks.

Common slips

• Reciting a definition without showing you have applied it.
• Using terms you cannot explain if asked a level deeper.
• Claiming experience beyond what you can discuss in detail.

How to practise it

Say the answer in 90 seconds, then answer the follow-up as if it were a new question.

Related questions

• Your document search assistant often answers using the wrong document. How would you debug and improve retrieval?
• A chatbot you built keeps giving long rambling answers. What would you change first?
• What is prompt injection, and how might it affect an assistant that reads customer emails?
• How do you evaluate whether a change to a prompt or model actually made your application better?
• Tell me about an agent or tool-using workflow you built. How did you stop it from looping or taking the wrong action?

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