41 RAG Interview Questions

All 41 questions

How would you evaluate a RAG system using customer-specific test cases and metrics?
Asked in 5 of 291 interviews · last asked Oct 7 · Concept · Mid-level
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I would build a test set for each customer using real questions, trusted answers, and expected source documents. I would test retrieval and answer quality separately, including questions the documents cannot answer. I would also measure latency and cost under expected traffic, set customer-specific targets, and rerun tests after changes.
- Measure retrieval recall@k: the fraction of relevant documents found in the top k results.
- Score answer correctness, support from sources, citation accuracy, and correct refusal when evidence is missing.
- Include customer terms, access rules, and hard cases; have domain experts review answers.
- Track tail latency, throughput, error rates, and cost per query under load.
What the interviewer checks: The interviewer checks whether you can turn customer needs into repeatable tests for quality and production performance.
Other ways it was asked
- “How would you evaluate a RAG pipeline for each customer?”
- “How would you deploy and evaluate a RAG system, including its accuracy, latency, and performance?”
- “What are some methods for evaluating a RAG system?”
What is RAG, how does it work, and how does it differ from using an LLM alone?
Asked in 3 of 291 interviews · last asked Sep 22 · Concept · Junior
Show answer
RAG means retrieval-augmented generation: it retrieves relevant information and gives it to an LLM to help answer a question. Documents are usually split into chunks and indexed; matching chunks are added to the prompt. An LLM alone relies on its learned knowledge and given prompt, without retrieving sources. RAG can use current or private information without retraining the model.
- An LLM generates text; RAG is a system that adds retrieval around an LLM.
- Retrieval can use keyword search, vector search, or both.
- RAG can support source citations, but poor retrieval can still produce wrong answers.
- Retrieval adds latency, cost, and system complexity.
What the interviewer checks: The interviewer checks whether you understand the retrieval-to-generation flow, its benefits, and its limits.
Other ways it was asked
- “What is RAG, how is it architected, and how does it differ from using an LLM without retrieval?”
- “What are large language models (LLMs) and retrieval-augmented generation (RAG)?”
- “What are large language models (LLMs) and retrieval-augmented generation (RAG), and how do they differ?”
How do you monitor a RAG system in production over time, and what do you do if you find issues?
Asked once · last asked Oct 14 · Scenario · Senior
Other ways it was asked
- “How do you monitor a RAG system in production, identify issues, and address them?”
What is chunking, and how is it related to the context window?
Asked once · last asked Feb 5 · Concept · Junior
How would you evaluate a retrieval pipeline after embedding and reranking, and which metrics would you use?
Asked once · last asked May 12 · Scenario · Senior
How does RAG help reduce hallucinations?
Asked once · last asked Jun 10 · Concept · Mid-level
Which metrics would you use to measure reductions in hallucinations and improvements in retrieval accuracy?
Asked once · last asked Jun 29 · Concept · Mid-level
How does the Ragas framework work, particularly for measuring faithfulness?
Asked once · last asked Jun 29 · Concept · Mid-level
How would you test a chatbot's data-chunking strategy to ensure it does not degrade answer quality?
Asked once · last asked Jul 16 · Scenario · Mid-level
Can you explain RAG and LLMs, including LLM hallucinations?
Asked once · last asked Jul 17 · Concept · Junior
What's your evaluation strategy for retrieval?
Asked once · last asked Jul 20 · Scenario · Mid-level
How would you build an evaluation dataset for a RAG system that retrieves drug aliases?
Asked once · last asked Jul 24 · Scenario · Senior
Why are citations important in a RAG workflow, and what happens without them?
Asked once · last asked Jul 27 · Concept · Mid-level
How would you provide an LLM with information about relationships within a large dataset without overwhelming its context window?
Asked once · last asked Aug 7 · Design · Senior
Other ways it was asked
- “How would you give an LLM the interconnectedness of a large dataset without overwhelming it?”
Why does a RAG system typically retrieve document chunks instead of passing entire documents to the model?
Asked once · last asked Aug 13 · Concept · Mid-level
How would you handle context in a document question-answering system?
Asked once · last asked Aug 13 · Design · Mid-level
How would you design a RAG system end to end, reduce hallucinations, and evaluate its performance?
Asked once · last asked Aug 22 · Design · Senior
How do you evaluate and tune your RAG setup end to end for retrieval quality and answer quality, and what offline metrics and test methodology do you rely on?
Asked once · last asked Aug 24 · Design · Senior
How would you build an offline evaluation set and scoring rubric for a RAG question-answering feature that reliably detects regressions in groundedness and usefulness?
Asked once · last asked Aug 24 · Design · Senior
Why not send all 10,000 documents to the LLM with every question?
Asked once · last asked Sep 5 · Concept · Mid-level
How would you establish the correct answers to use as ground truth when evaluating a RAG pipeline?
Asked once · last asked Sep 10 · Scenario · Senior
If staff wanted to ask an AI agent questions about farm benchmark results, how would you prepare and align the underlying data, and check that the answers were trustworthy and supported by the data?
Asked once · last asked Sep 23 · Design · Senior
How would you architect a healthcare RAG system that ingests patient medical histories and recommends tests, medications, or supplements?
Asked once · last asked Sep 24 · Design · Senior
Your RAG system has 95% retrieval recall, but users still complain about incorrect answers. How would you investigate?
Asked once · last asked Sep 27 · Scenario · Senior
What would you do if a RAG system retrieves the right document but the LLM still generates an incorrect answer?
Asked once · last asked Sep 30 · Scenario · Senior
How would you evaluate reranking and retrieval approaches as potential solutions for an enterprise AI platform?
Asked once · last asked Oct 7 · Scenario · Senior
How would you determine whether poor RAG results come from retrieval or generation?
Asked once · last asked Oct 7 · Scenario · Senior
What is your process for validating and testing a RAG solution before putting it into production?
Asked once · last asked Oct 14 · Scenario · Senior
How would you evaluate the quality of answers produced by a RAG system?
Asked once · last asked Oct 20 · Behavioral · Mid-level
Other ways it was asked
- “How did you evaluate the quality of answers produced by a RAG system?”
How do you ensure the particular cited page actually supports the answer?
Asked once · last asked Oct 22 · Scenario · Mid-level
If an enterprise RAG system answers confidently but incorrectly even though the information is present in the document, how would you investigate the problem?
Asked once · last asked Oct 22 · Scenario · Senior
How would you determine whether an incorrect RAG answer comes from retrieval, chunk size, the prompt, or the language model?
Asked once · last asked Oct 22 · Scenario · Senior
Other ways it was asked
- “How would you determine whether the problem comes from retrieval, chunk size, the prompt, or the language model?”
How would you test whether the final answer is supported by citations?
Asked once · last asked Oct 22 · Scenario · Mid-level
How would you test questions that cannot be answered from the documents?
Asked once · last asked Oct 22 · Scenario · Mid-level
How would you determine whether the new chunk-size configuration is actually better?
Asked once · last asked Oct 22 · Scenario · Mid-level
What are the trade-offs of restricting an AI system's answers to retrieved data, and what alternative approaches might you consider?
Asked once · last asked Oct 23 · Concept · Senior
How might you limit the context supplied to an LLM in a RAG application based on its use case?
Asked once · last asked Apr 24 · Scenario · Mid-level
What do you know about RAG agents and Gemini?
Asked once · last asked Jul 16 · Concept · Mid-level
How can you verify RAG evaluation results against the source documents?
Asked once · last asked Sep 10 · Scenario · Mid-level
Can the LLM step be skipped in a retrieval-augmented generation (RAG) workflow?
Asked once · last asked Sep 22 · Concept · Mid-level
What deliverable would demonstrate that a RAG evaluation was successful?
Asked once · last asked Oct 22 · Scenario · Mid-level

How this list is made

Every question here was asked by a real interviewer in a real job interview that a candidate ran MeetAssist in. After the call, the interviewer's questions are pulled from the transcript. Practice sessions, tests, exams and anything that was not a job interview are dropped.

Questions are rewritten so they make sense on their own: names of people, employers and clients are removed, and speech-to-text mistakes are fixed. Small talk, logistics and questions about one candidate's own résumé are left out. Rephrasings of the same question are merged, and the count is the number of different interviews it came up in.

The interviews come from candidates who use MeetAssist, so they lean towards software, data and AI roles: the shares describe these interviews, not every Generative AI and LLM interview. Answers to the most asked questions are written and checked by an editor; a "common mistake" is shown only when reviews of real answers in these interviews show it, with the count.

291 interviews, 178 candidates, Feb 2026 to Oct 2026. Updated every month with new interviews.

All 41 questions

How would you evaluate a RAG system using customer-specific test cases and metrics?

What is RAG, how does it work, and how does it differ from using an LLM alone?

How do you monitor a RAG system in production over time, and what do you do if you find issues?

What is chunking, and how is it related to the context window?

How would you evaluate a retrieval pipeline after embedding and reranking, and which metrics would you use?

How does RAG help reduce hallucinations?

Which metrics would you use to measure reductions in hallucinations and improvements in retrieval accuracy?

How does the Ragas framework work, particularly for measuring faithfulness?

How would you test a chatbot's data-chunking strategy to ensure it does not degrade answer quality?

Can you explain RAG and LLMs, including LLM hallucinations?

What's your evaluation strategy for retrieval?

How would you build an evaluation dataset for a RAG system that retrieves drug aliases?

Why are citations important in a RAG workflow, and what happens without them?

How would you provide an LLM with information about relationships within a large dataset without overwhelming its context window?

Why does a RAG system typically retrieve document chunks instead of passing entire documents to the model?

How would you handle context in a document question-answering system?

How would you design a RAG system end to end, reduce hallucinations, and evaluate its performance?

How do you evaluate and tune your RAG setup end to end for retrieval quality and answer quality, and what offline metrics and test methodology do you rely on?

How would you build an offline evaluation set and scoring rubric for a RAG question-answering feature that reliably detects regressions in groundedness and usefulness?

Why not send all 10,000 documents to the LLM with every question?

How would you establish the correct answers to use as ground truth when evaluating a RAG pipeline?

If staff wanted to ask an AI agent questions about farm benchmark results, how would you prepare and align the underlying data, and check that the answers were trustworthy and supported by the data?

How would you architect a healthcare RAG system that ingests patient medical histories and recommends tests, medications, or supplements?

Your RAG system has 95% retrieval recall, but users still complain about incorrect answers. How would you investigate?

What would you do if a RAG system retrieves the right document but the LLM still generates an incorrect answer?

How would you evaluate reranking and retrieval approaches as potential solutions for an enterprise AI platform?

How would you determine whether poor RAG results come from retrieval or generation?

What is your process for validating and testing a RAG solution before putting it into production?

How would you evaluate the quality of answers produced by a RAG system?

How do you ensure the particular cited page actually supports the answer?

If an enterprise RAG system answers confidently but incorrectly even though the information is present in the document, how would you investigate the problem?

How would you determine whether an incorrect RAG answer comes from retrieval, chunk size, the prompt, or the language model?

How would you test whether the final answer is supported by citations?

How would you test questions that cannot be answered from the documents?

How would you determine whether the new chunk-size configuration is actually better?

What are the trade-offs of restricting an AI system's answers to retrieved data, and what alternative approaches might you consider?

How might you limit the context supplied to an LLM in a RAG application based on its use case?

What do you know about RAG agents and Gemini?

How can you verify RAG evaluation results against the source documents?

Can the LLM step be skipped in a retrieval-augmented generation (RAG) workflow?

What deliverable would demonstrate that a RAG evaluation was successful?

More Generative AI and LLM interview questions

How this list is made

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