AI Interview Questions for Candidates and How to Answer
The AI, LLM and generative AI topics interviewers ask about, with plain definitions, a RAG answer and a one-week preparation plan.
Last updated: 21 September 2026 · By the Asuraa Team
What are AI interview questions?
AI interview questions are the questions you are asked about artificial intelligence, machine learning and large language models when you apply for a role that uses them. DataCamp's generative AI interview guide (updated 22 December 2025) splits them into basic generative AI questions and AI engineer-focused questions.
The phrase is also used for interviews conducted by an AI system. That is a different topic, and we cover it in one short section near the end of this post.
Which topics do AI interview questions cover?
They cover concepts, applications and production concerns. DataCamp lists discriminative versus generative models, tokens and embeddings, GANs, real-world applications, training and evaluation challenges and inference optimisation among the basics.
For AI engineer roles it adds agentic workflows versus chatbots, safety and guardrails, production challenges, context window limits and RAG architecture. Use the table to see where each area sits.
| Area | Example question | Where to read more |
|---|---|---|
| Foundations | What is the difference between a discriminative and a generative model? | DataCamp's basic section |
| Language models | What are tokens and embeddings? | Google's ML glossary |
| Applications | Where would you use an LLM, and where not? | Your own project experience |
| Retrieval | What is RAG and what problem does it solve? | Google Cloud's RAG page |
| Reliability | How would you reduce hallucinations? | Google's ML glossary and RAG page |
| Fundamentals | What is overfitting? | Our machine learning interview questions |
The example questions are typical of the topics DataCamp lists, and the last column is our suggestion.
What do the key terms mean?
Learn the plain-English definition of each term first. Google's machine learning glossary supplies most of them.
| Term | Definition (as the glossary puts it) |
|---|---|
| Token | The smallest unit of text a language model processes: a word, subword or character |
| Embedding | A representation of text or other data as a vector of numbers, where similar items have similar values |
| Context window | The number of tokens a model can process in a given prompt |
| Fine-tuning | A second training phase that adapts a pre-trained model to a specific task with task-specific labelled data |
| Prompt engineering | Refining text prompts to make large language models more effective at generating useful responses |
| Hallucination | Output from a generative AI system that is factually incorrect but presented as if true |
Say these in your own words, then add one example. For a token, you might say that the word "unwatched" could be split into smaller pieces, which matches the subword idea in Google's crash course on language models.
How do you explain RAG in an interview?
Retrieval-augmented generation combines search with a language model so answers are grounded in your own data. Google Cloud's RAG overview says it combines information retrieval systems, such as search and databases, with generative LLMs.
It describes two main steps: retrieving relevant information from external data, then feeding that information to the model so it can generate a more accurate response. Google Cloud says RAG addresses stale knowledge, because models are limited to their pre-trained data, and hallucinations, because providing facts in the prompt mitigates them.
Here is an illustrative answer for a fresher, written by us.
"A language model only knows what it saw in training, so it can be out of date or make things up. RAG first searches a knowledge base for relevant passages, then gives those passages to the model along with the question. That way the answer is based on real sources. It reduces hallucinations, but I would still test the answers, because retrieval can bring back the wrong passage."
The last sentence matters. It shows you know RAG is a mitigation and not a guarantee, which is our own caution and not a claim from the source.
How do you answer "how would you reduce hallucinations"?
Give a short list of measures, then say how you would check they work. This is a good place to show engineering judgement instead of buzzwords.
- Ground the answer. Retrieve relevant facts and put them in the prompt, as RAG does.
- Limit the task. Ask for answers only from the provided sources, and say "I do not know" when the source is silent.
- Add checks. Compare outputs against known answers on a small test set.
- Keep a human in the loop for high-stakes outputs.
Steps 2 to 4 are our suggestions, and step 1 follows Google Cloud's description. DataCamp also notes that engineers should put explicit guardrails between the user and the model in production, which is worth mentioning for AI engineer roles.
Do you need to know ML basics as well?
Yes. Interviewers for AI roles still ask about data, evaluation and overfitting, because LLM applications sit on top of those ideas. Our guides to machine learning interview questions and Python interview questions cover the fundamentals.
If you are building skills for these roles, see how to learn generative AI and our post on AI engineer skills.
What if the interview itself is run by AI?
That is a separate topic. Some employers use software to ask questions or record answers, and it may score or summarise them. We cover how it works in our guide on what an AI interview is.
For your preparation, the basics do not change: prepare clear, honest answers, check the technology beforehand and ask the employer how the recording or scoring is used. Interview preparation is a feature that is coming soon on asuraa.in.
How should you prepare in a week?
Cover concepts first, then projects, then practice aloud. The plan is our suggestion.
| Days | Focus | Output |
|---|---|---|
| 1-2 | Key terms in the table above | A one-page glossary in your own words |
| 3 | RAG and hallucinations | A two-minute spoken explanation |
| 4 | ML fundamentals | Overfitting and metrics answers |
| 5 | Your AI project | A walkthrough of data, model, checks and limits |
| 6 | Practice aloud | Two mock answers, recorded |
| 7 | Light review | Notes read once, then rest |
What do most guides on AI interview questions get wrong?
Most guides give 40 to 85 questions with model answers. These are the gaps.
- They favour buzzwords. Interviewers look for whether you can explain an idea simply and name its limits.
- They skip the basics. ML fundamentals and Python still matter, so cover them too.
- They ignore your own work. A project you can explain end to end is stronger than a list of definitions.
- They miss the honesty question. You may be asked how you use AI tools, so be ready to say what you checked yourself.
FAQ
What are AI interview questions?
The phrase has two meanings. Most often it means questions candidates get about AI, machine learning and large language models, such as tokens, embeddings, RAG and hallucination. It can also mean an interview run by an AI system, which is a separate topic covered in our post on AI interviews.
What generative AI questions are asked in interviews?
DataCamp's guide (updated 22 December 2025) covers discriminative versus generative models, tokens and embeddings, real-world applications and evaluation challenges. For AI engineer roles it adds agentic workflows, guardrails, production issues, context windows and RAG. Which ones you face depends on the role.
What is a hallucination in a large language model?
Google's machine learning glossary defines a hallucination as output from a generative AI system that is factually incorrect but presented as if true. In an interview, add how you would reduce it, for example by grounding answers in retrieved sources, and how you would test for it.
What is RAG, and why is it asked about so often?
Google Cloud describes retrieval-augmented generation as combining information retrieval, such as search or databases, with a generative model so answers are grounded in your data. It is asked about because it addresses stale knowledge and hallucinations in practical LLM applications.
Do I need to know AI to get a non-technical job?
Not deeply, but you may be asked how you use AI tools at work. Prepare a short, honest example of a task where a tool helped you, how you checked its output and what you would not delegate to it. Do not claim skills you cannot demonstrate.
How should a fresher prepare for AI interview questions?
Learn the core terms, run small examples yourself and build one project you can explain end to end. Practise explaining ideas such as tokens and RAG to a non-technical listener. Also prepare for the fundamentals from our machine learning and Python question guides.
Final thoughts
AI interviews reward clear explanations, sound fundamentals and honest limits. Learn the key terms, build one project you can explain and practise saying it simply.
To see openings that match your skills, browse the AI jobs for freshers guide.
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