50 Generative AI Interview Questions and Answers (Beginner to Practitioner)

By Ravindra Bagale, trainer · LinkedIn · Last updated: 19 October 2026

Generative AI course · Chapter 52 · Estimated reading time: 16 minutes

1. Suppose the interview is for a Junior GenAI role

Suppose Karan has an interview at a software company in Pune for a "Junior GenAI Developer" role. The panel has 30 minutes. They will ask a few basics, one or two "what would you do" scenarios, and maybe ask him to draw a design on the whiteboard.

Karan does not need to memorise 500 answers. He needs:

1. Clear, short answers to the common questions (section 2).
2. A pattern for any concept question (example 1).
3. A pattern for any scenario question (example 2).
4. One design he can draw from memory (example 3).

Each answer below is 1 to 3 sentences. In the interview, add one example from your own project after it.

2. The 50 questions and answers

Basics (1 to 10)

1. What is generative AI? AI that creates new content, such as text, images, audio or code, from a prompt. It learns patterns from large amounts of data and produces something new that fits those patterns.
2. How is it different from traditional AI? Traditional AI mostly classifies or predicts (spam or not spam, next month's sales). Generative AI produces new content.
3. What is an LLM? A large language model: a model trained on a lot of text to predict the next token. Chat apps like ChatGPT, Gemini and Claude are built on LLMs.
4. What is a token? A small piece of text, often part of a word. Models read, write and charge by tokens.
5. What is a context window? The amount of text, in tokens, a model can consider at once, including your prompt, files and its answer.
6. What is a foundation model? A large model trained on broad data that can be adapted to many tasks with prompts, RAG or fine-tuning.
7. What is the difference between a model and an app? The model is the engine; the app (for example a chat app) adds the screen, memory, files, safety rules and tools around it.
8. What is temperature? A setting that controls randomness. Low temperature gives focused, repeatable answers; high temperature gives more varied answers.
9. What is a knowledge cutoff? The date after which the model has no training data. For newer facts it needs web search or your documents.
10. What is multimodal AI? A model that accepts more than one type of input, such as text, images, audio or PDFs, in the same chat.

Prompting (11 to 18)

11. What makes a good prompt? Role, task, context and format: who the AI should act as, what to do, the facts it needs, and the shape of the answer.
12. What is zero-shot and few-shot prompting? Zero-shot gives only the instruction. Few-shot adds 2 or 3 examples so the model copies the style or format.
13. What is step-by-step prompting? Asking the model to break a problem into steps, or using a reasoning or "thinking" mode, so it plans before it answers.
14. How do you get structured output? Ask for an exact format, such as a table or JSON with named fields, give one example, and validate the result in code.
15. What is a system prompt? Instructions that set the model's rules and role for the whole conversation, before the user's messages.
16. How do you reduce wrong answers in a prompt? Give the facts, say "answer only from the text below", allow "I don't know", and ask for sources.
17. What is prompt injection? Hidden or malicious instructions inside user input or a web page that try to make the AI ignore its rules.
18. How do you test a prompt? Run it on a fixed set of inputs with known good answers, compare the outputs, change one thing at a time, and keep the best version.

How models behave (19 to 26)

19. What is a hallucination? A confident answer that is false or made up, such as a fake book title or a wrong date.
20. Why do models hallucinate? They predict likely text, not checked facts. When they lack the facts, a likely-sounding answer can still be wrong.
21. How do you fact-check AI output? Check names, numbers and dates against the original source, use tools with citations, and never trust a link you have not opened.
22. What are embeddings? Lists of numbers that represent the meaning of text. Similar meanings have embeddings that are close together.
23. What is cosine similarity? A common way to measure how close two embeddings are; higher means more similar meaning.
24. What is fine-tuning? Training an existing model further on your own examples to change its style or behaviour.
25. Fine-tuning, RAG or prompting: how do you choose? Start with prompting. Use RAG when answers must come from your own changing documents. Use fine-tuning for a fixed style or format that prompts cannot hold.
26. What is an AI detector, and is it reliable? A tool that guesses whether text was AI-written. Its results are not proof; it can wrongly flag human writing.

RAG and vector databases (27 to 34)

27. What is RAG? Retrieval-augmented generation: find the relevant pieces of your documents first, then let the model answer from those pieces.
28. What are the RAG steps? Load, chunk, embed and store; then at question time retrieve the closest chunks and generate an answer with citations.
29. Why chunk documents? Models and search work better on small focused pieces than on a whole long PDF.
30. What chunk size do you use? It depends on the document; start with a few hundred words with some overlap, then test with real questions and adjust.
31. What is a vector database? A store that keeps embeddings with their text and labels and finds the closest ones fast. Examples: Chroma, pgvector, OpenSearch.
32. How do you know your RAG bot is good? Test it with a set of questions whose answers you know, check that the cited source is correct, and track wrong or missing answers.
33. What if the answer is not in the documents? The bot should say it does not know and suggest who to ask, not guess.
34. How do you do RAG on AWS? Amazon Bedrock Knowledge Bases: files in S3, an embedding model, a vector store such as S3 Vectors, and the retrieve-and-generate API with citations.

Agents and automation (35 to 40)

35. What is an AI agent? A model that works in a loop with tools: it decides an action, uses a tool, reads the result and continues until the goal is done.
36. Agent vs chatbot? A chatbot only replies. An agent can act, such as searching, calculating or creating a ticket.
37. What is tool calling? The model returns a structured request to run a named tool with arguments; your code runs it and sends the result back.
38. What guardrails does an agent need? An allow-list of tools, human approval before risky actions, a step limit, input checks and a log of every step.
39. What is MCP? The Model Context Protocol, an open standard for connecting AI apps to tools and data through servers, so one connector works in many apps.
40. When would you use n8n instead of code? For workflows that join common apps (email, sheets, chat) quickly, where a visual flow is easier to maintain than custom code.

Safety and responsible use (41 to 45)

41. What should never go into a public AI tool? Passwords, OTPs, bank details, Aadhaar or PAN numbers, client data and confidential company files.
42. How do you handle private data in an AI app? Use placeholders in prompts, keep data in your own account or cloud, limit access, and follow the company's AI policy.
43. What is a deepfake, and how do you check one? AI-made or altered video, photo or voice that looks real. Check the source, look for odd details, and confirm through a second trusted channel.
44. Who owns AI-generated content? It depends on the tool's terms and the law in your country; check the terms and do not copy protected work or real people's likeness without permission.
45. What is bias in AI, and how do you reduce it? Unfair results for some groups, often from training data. Reduce it with careful prompts, diverse test cases and human review.

Building on AWS and in production (46 to 50)

46. What is Amazon Bedrock? An AWS service that gives access to many foundation models through one API, with AWS security and billing.
47. How do you control cost? Pick the smallest model that works, set a maximum output length, cache repeated answers, and set a budget alert.
48. How do you keep an API key safe? Store it in environment variables or a secrets manager, never in code or screenshots, and rotate it if it leaks.
49. How do you evaluate a GenAI feature? A fixed test set with expected answers, a simple score (correct, partly correct, wrong), human review of samples, and tracking after launch.
50. What would you do in your first month on a GenAI team? Learn the product and data, run the existing test set, fix one small issue end to end, and write down what you learned.

3. Worked example 1: answer a concept question

Question: "What is RAG?"

A weak answer is one line from memory. A strong answer follows Define → Example → Limit → My project:

Define:     RAG means the system first finds the right parts of our documents,
            then the model answers only from those parts.
Example:    For an HR policy bot, the question "How many casual leaves?" finds the
            leave section of the PDF, and the answer cites that page.
Limit:      If the search finds the wrong chunk, the answer is wrong, so we test
            retrieval with known questions.
My project: I built this with Chroma and tested 10 questions; 9 were right at first,
            and smaller chunks fixed the last one.

Check your answer with AI:

Act as an interviewer for a Junior GenAI Developer role. Here is my answer to
"What is RAG?": [paste]. Score it out of 5 for correctness, clarity and example.
Say one thing to cut and one thing to add. Do not rewrite it for me.

Expected output (yours will be similar, not the same): a score such as "4/5", one line on what was clear, and one suggestion, for example "Name the vector database you used." Say the improved answer out loud twice.

4. Worked example 2: answer a scenario question

Question: "Our support chatbot gives confident wrong answers about refunds. What would you do?"

Use Clarify → Find the cause → Fix → Measure:

1. Clarify: "Is the bot answering from our refund policy, or from the model's own knowledge?"
2. Find the cause: check a few wrong answers. If it has no policy text, it is guessing. If it has the text, retrieval may be picking the wrong part.
3. Fix: add RAG over the current refund policy, tell it to answer only from the retrieved text, show the source, and say "I don't know, please contact support" when nothing matches.
4. Measure: build 20 refund questions with known answers, run them before and after, and report the change.

Expected output: practise with the prompt below. A good AI interviewer will ask a follow-up such as "What if the policy changes every month?" Your answer: sync the knowledge base when the document changes and keep only the current version.

You are a GenAI interviewer. Ask me one scenario question about a chatbot problem.
Wait for my answer. Then ask one follow-up question. Then give feedback in 3 bullet points.

5. Worked example 3: a whiteboard RAG design

Question: "Draw a system where employees ask questions about 200 HR PDFs."

Draw it top to bottom and say each box out loud:

[HR PDFs in a private store]  -> only current, approved versions
          |
[Ingest: split into chunks, make embeddings]  -> runs when a file changes
          |
[Vector database]  -> chunks + embeddings + labels (department, date)
          |
[Employee question] -> [Retrieve top 4 chunks] -> [Model writes answer + citation]
          |
[Checks: no source = "I don't know"; log question, answer and sources]

Then name the trade-offs the panel wants to hear:

Expected output: a clear 6-box drawing and 3 to 5 trade-offs, explained in about 3 minutes. Time yourself once.

6. A daily 5-question mock round (Python)

Paste the 50 questions into a text file, one per line, and let this script pick 5 at random. A fixed seed makes the same 5 come up for everyone reading this example; change it to any number for a new round.

import random
questions = open("questions.txt", encoding="utf-8").read().strip().splitlines()
random.seed(7)                      # change 7 to any number for a new round
round_ = random.sample(range(len(questions)), 5)
print(f"Mock round: 5 of {len(questions)} questions. Answer each aloud in under 1 minute.")
for n, i in enumerate(round_, 1):
    print(f"{n}. (Q{i + 1}) {questions[i]}")

Real output (19 October 2026, Python 3.13, with the 50 questions of section 2 in questions.txt):

Mock round: 5 of 50 questions. Answer each aloud in under 1 minute.
1. (Q21) How do you fact-check AI output?
2. (Q10) What is multimodal AI?
3. (Q26) What is an AI detector, and is it reliable?
4. (Q42) How do you handle private data in an AI app?
5. (Q4) What is a token?

7. Beginner mistakes and quick fixes

8. Try at home

Recap

Pick 5 questions, answer them aloud today, and repeat tomorrow with a new seed. Ten days of this is better than one night of reading.

Previous chapter: AI Jobs in India for Freshers: Roles, Skills and 3 Portfolio Projects

Back to: Generative AI course home

Ravindra Bagale, trainer: linkedin.com/in/ravindra-bagale. Karan and his interview are made up for learning. The answers summarise what this course teaches; tool and service names were checked on 19 October 2026. The Python output was produced on 19 October 2026. AI output changes every time, so your output will be similar, not the same.