30 Gen AI Interview Questions — Step-by-Step Guide & Transcript

[00:07] interview, knowing definitions alone probably won't be enough. An interviewer may start with something very simple like what is generative AI? But very quickly the discussion can move towards LLMs, transformers, embeddings, rag,

[00:22] vector databases, fine-tuning, hallucinations, AI agents, evaluation, and even real world system design. And then comes the most interesting part, scenario-based questions. You might hear something like this. Imagine our company

[00:37] has thousands of internal documents. How would you build an AI assistant that answers employees questions accurately? That's where interviewers are not just checking what you know. They want to understand how you think. So in this

[00:51] video, we'll go through generative AI interview questions across beginner, intermediate, and advanced levels. And finally, we will solve practical case studies that are very similar to what you may face in the real interview. So

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[02:17] AI first economy, then explore the program and start your learning journey today with simply learn. So let's start our video with a basic quiz question and the question is what is primary capability of generative AI and your

[02:31] options are creating new content from learned patterns option B only storing large amounts of data option C only classifying existing information or the option D replacing every traditional AI model. So now without any further ado

[02:45] let's tap into the world of generative AI interviews. So now let's start our video with the beginner level generative AI interview questions. So this is the most basic and most predicted question in any interview and you should know

[03:00] this at least the basics and that is what is generative air. So you can answer this question like this. So generator is a type of artificial intelligence that can create new content based on patterns it has learned from

[03:13] existing data. That content could be text, images, audio, video, code, and even 3D content. For example, if I ask GPT to write an email, it generates text. If I use an image generation model and describe a future, if I use an image

[03:30] generation model and describe a futuristic city, it generates an image. So instead of only classifying or predicting something, generative AI actually creates something new. So now let's move on to our second question

[03:43] that is how is generative AI different from traditional AI. So you as a candidate can answer this question like this. Traditional AI is usually focused on prediction or classification. For example, an AI model might predict

[03:58] whether a transaction is fraudulent or classify an email as spam or ham. Generate AI goes one step further. Instead of saying this email is spam, it

[04:10] could actually generate an email, summarize it, rewrite it or respond to it. So I would say traditional AI is mostly answering what is this or what will happen. Generative AI can answer what can I create based on what I know.

[04:25] So this is how you can explain the difference between generative AI and traditional AI by giving the definition then the difference between both and the examples and then the conclusion. So this should be your approach for

[04:38] answering any of the questions in your interview. Now let's move on to the third question that is what is an LLM? So LLM stands for a large language model. It is a machine learning model trained on very large amount of text so

[04:52] that it can understand and generate humanlike language. For examples, claw, gemini, llama and other language model. At a simple level, an LLM predicts the next token based on the token that came before it. But when you scale that

[05:08] training across massive data sets and very large neural networks, the model becomes capable of task like summarization, translation, coding, question answering, reasoning and content generation. So this is what LLM

[05:23] is. Now moving on to our fourth question that you can expect in the beginner level generative AI interviews and that is what is a token? So a token is basically a small unit of text that a language model processes. It may be a

[05:38] complete word, part of a word, punctuation or another text fragment. So when we send a prompt to LLM, the model doesn't directly see complete sentence the way humans do. The text is first broken into tokens and this is also

[05:54] important because LM usage context windows and API cost are often measured windows and API cost are often measured using tokens. So this is what a token in using tokens. So this is what a token in brief. Be pointto-point be precise be

[06:07] practical and try explaining every question through an example. This is a good approach you can follow in any of your interviews. Now moving on to the next question. What is a prompt? Prompt is the instruction or input we give to a

[06:20] generative AI model. For example, explain machine learning to a 10year-old. That is a simple prompt. But a better prompt might include context, a specific role, instructions, constraints, and an

[06:34] expected output format. You are a machine learning instructor. Explain supervised learning to a beginner in undertone words and include one real world example. So the second prompt gives the model much clearer direction.

[06:49] gives the model much clearer direction. Is it right? You only can understand the simple prompt that is displayed on the screen and then the second prompt. Which is better? Of course the second one. So this is how you can explain what is a

[07:03] prompt. Next question. What is prompt engineering? So prompt engineering is the process of designing prompts in a simple way that helps an AI model produce more accurate, relevant and consistent results. It can involve

[07:17] giving the model context examples, constraints, rules, output formats, and stepbystep instructions. For example, instead of saying write a report, I might say act as a business analyst. Write a 500word report comparing company

[07:33] Write a 500word report comparing company A and company B based on revenue growth, profitability, risk, and future opportunities. Written the answer with four headings that usually produces a much more useful result. Now the next

[07:47] question, what is a transformer? This is also the expected question in any beginner level generative AI interviews. So you can answer like this. The trust behind most modern large language models. The important idea behind

[08:03] transformers is something called attention. Attention allows the model to understand which words or tokens in the input are important relative to one another. For example, in a long sentence, the model can identify which

[08:16] earlier word are relevant to understand the current word. So transformers are also highly paralyzable which made it possible to train extremely large language models effectively. So this is basically what a transformer is. Now

[08:31] next move on to attention because we learned a word called attention in this slide right. So next question is what is attention? So attention helps the model decide which parts of the input it should focus on when processing a token.

[08:46] Suppose I say John gave Rahul his laptop because he was leaving the company. So to understand what different words refer to, the model needs to look at relationship between words across the sentence. Attention allows the model to

[09:00] calculate those relationships instead of processing every word independently. Now what is an embedding? So an embedding is a numerical representation of information for text. An embedding converts words, sentences or documents

[09:16] into vectors. Basically, list of numbers. The useful thing is that semantically similar content usually gets placed closer together in vector space. Let's understand this with an example. So the example stays how do I

[09:31] example. So the example stays how do I reset my password or I forgot my login password would probably have similar embeddings even though the exact words are different. that makes embeddings extremely useful for semantic search and

[09:44] rag applications. Now let's move on to the next question that is what is hallucination in generative AI. So hallucination happens when an AI model generates information that sounds confident and believable but is actually

[10:00] confident and believable but is actually incorrect or unsupported. For example, an LLM may invent a research paper, company policy, statistics or citation. This happens because an LLM generates likely sequence of tokens. It it isn't

[10:15] automatically verifying every statement against a reliable source. So we can reduce hallucination using techniques like rag better prompts grounding

[10:27] grouping structured output tool tool calling validation and asking the model to say when it doesn't know something. This is how you can reduce hallucination in your model. So now let's take a break. Go have your water because we

[10:43] have completed the beginner level interview question. Come back as soon as possible because we are going to start with intermediate generative AI interview questions. I hope I made you very clear about the questions you can

[10:56] expect in your foundation level. Going forward we will look all the possible questions in your interview that is intermediate level. So let's get started. So the first question is what is REG? This is the most basic

[11:10] intermediate level interview question for a generative AI interviews. So as a candidate you can say REAG stands for retrieval argument generation that is the full form of RAG. Then the idea is that instead of asking an LLM to answer

[11:24] purely from what it learned during training, we first retrieve relevant information from an external knowledge source, then give that information to the model. For example, imagine a company has 50,000 internal documents.

[11:38] If an employee asks, "What is our parental leave policy?" The system can search the company documents, retrieve the relevant HR policy, provide it to the LLM, and then generate the answer based on that document. So the basic

[11:53] flow is this. Use a question, then retrieve relevant information, then add context to the prompt, then LLM generates the answers. So this is what rag was. So the definition then the example and then the basic flow. I hope

[12:10] you are getting the context. Any question you expect in your interview, you should have a format for answering that question. First the definition of that question, then the example and then the explanation. This will give a better

[12:24] idea to the recruiter and your chances of getting selected increases automatically. and don't make it very lengthy or something. Try to be at point and try to answer in as precise manner as possible. Now the next question you

[12:39] may expect in the interview is why would you use rag instead of fine-tuning. So you can say like this. I wouldn't automatically choose one over the other because they solve different problems. I would use rag when the model needs

[12:54] access to frequently changing or private knowledge. for example, a company policy, product documentation, customer records or research paper. Whereas fine-tuning is more suitable when I want to change how the model behaves. For

[13:09] example, maybe I want a specific writing style, classification behavior, response format or domain specific behavior. So a simple way is to remember like this rag

[13:21] is mainly about giving the model knowledge. Fine-tuning is mainly about changing model behavior and in many production applications you can use both actually. Now the next question what is a vector database? So a vector database

[13:37] stores embeddings and help us search for semantically similar information. Instead of searching only by exert keywords we can search based on meaning. For example, if a user ask how can I recover my account? The database may

[13:53] retrieve a document called think forgotten password. Even if the exact words recover my account do not appear there. Some common vector databases and databases search techniques include spine core or chroma cordant fias and

[14:09] vector capabilities available in other databases. Now what is chunking in rags? Chunking means splitting large documents into smaller pieces before generating

[14:21] embeddings. We do this because embeddings can entire 200page document as one vector would make retrieval very inaccurate. Instead, we might break the document into paragraphs, sections or chunks for a few hundred tokens. Then

[14:37] when someone ask a question, the system retrieves only the most relevant chunks. So the challenge here is choosing the right chunk size. Chunks that are too small may lose context. Chunks that are too large may contain too much

[14:53] irrelevant information. So chunking strategy can significantly affect rack performance. Now what is semantic search? This is also the most expected questions in any of your intermediate level generative AI interviews. So go

[15:09] through this. So semantic search searches based on meaning rather than only exact keyword matches. For example, affordable hotels near the airport and cheap accommodation close to the airport have similar meaning. Right? A normal

[15:23] keyword search may struggle if the words are completely different. Semantic search uses embeddings to identify that the two queries are conceptually related. So this is the difference between semantic search and the normal

[15:36] search. Now the next question, what is temperature in an LLM? So temperature controls how deterministic a creative the model's output is. With the lower temperature, the model usually gives more predictable and consistent

[15:51] responses. With higher temperature, the model has more randomness and may generate more diverse outputs. So for something like financial data extraction and structured classification I would

[16:04] probably use a low temperature. For brainstorming creative marketing ideas a higher temperature may be useful. Now coming to our 17th question that is what coming to our 17th question that is what is a context window? So a context window

[16:18] is basically the amount of information the model can consider during a single interaction. It includes things such as system instructions, conversation history, retrieve documents, tool outputs, and user prompts. If the

[16:32] information exceeds the supported context window, we need strategies like summarization, retrieval, filtering, and conversation memory management. Now, what is zeroshot prompting? We understood prompts, right? So, the next

[16:47] question you might get is what is zeroshot prompting? It might look very easy but you need to explain in a very systematic way. I'll show you how. So first start with the definition. Zeroshot prompting means asking the

[17:01] model to perform without giving it example. For example, classify this reviews as positive, negatives or neutral and then I give it the review. The model has to understand the task directly from the instructions. Now what

[17:16] is few short prompting? So few short prompting means giving the model a few examples before asking it to perform any specific task. Previously we just asked him the question and given no example. Right? Here we will give few examples to

[17:31] Right? Here we will give few examples to it first. For example, review amazing service sentiment positive. Review the product broke after one day sentiment negative. Then I provide a new review and ask the model to classify it. So

[17:46] example helps model understand the expected pattern and the output format. So this was the basic difference between both the prompings. So let's get back ahead in the time and try to understand what is exactly a fine-tuning. This is

[18:01] also the question you can expect in the interviews. So fine-tuning means taking an already trained model and training it further using a smaller specialized data set. The idea is not to train the entire language model from scratch. Instead, we

[18:16] adapt an existing model for a particular task or behavior. For example, a company might fine-tune a model using examples of high quality customer support response so that it consistently follows that organization's tone and response

[18:32] time. So these were some most basic and most expected intermediate level interview questions for your next generative AI interviews. Go through all of these. Take the screenshot for your future revisions. And if you have any

[18:46] doubt or any queries related to any of the questions you can immediately ping team of experts will try to reach you as soon as possible. Or any other kind of a

[18:58] help you want in your interviews. We are always there. Just ping your question your queries under the comment section. Now one level ahead advanced generative AI interview questions. If you want to take break go take a break. You deserve

[19:13] this break. Come again then we will start with the third level. So explain the complete rag architecture. This is where your advanced level interview mainly starts. This is the most expected questions in any of your interviews. So

[19:28] prepare it thoroughly. I'm reminding you I'm reminding you guys prepare it thoroughly. This is the most expected question. So you can answer this question in the interview like this. I would divide a rag system into two major

[19:43] would divide a rag system into two major stages. The first stage is ingestion. We take documents, clean them, split them into chunks, generate embeddings and store those embeddings along with useful meta data in a vector database. The

[19:57] second is query processing. When the user asks something, we create an embedding for the query and search the vector database for similar chunks. You may then apply metadata filters or reranking to improve relevance. The best

[20:14] retrieved chunks are included in the prompt along with the user's question. The LLM then generates a final response grounded in that context. So conceptually documents cleaning chunking embedding and then back to database

[20:31] that's not enough then user query query embeddings retrieval ranking prompt llm and response this is how the actual flow look like next question how would you

[20:43] improve a poorly performing rag system I would avoid immediately changing the language model because the problem could be somewhere else first I would check retrieval quality After we retrieve the correct documents,

[20:56] then I would look at chunking. Maybe the chunks are too large or too small. I would check the embedding models and whether the query and documents are represented correctly. Next, I could test hybrid search where we combine

[21:11] semantic search with keyword search. I might also use reranking to reorder retrieved results. Then I would examine the prompt to see whether the model is clearly instructed to answer only from the provided context. And finally I

[21:27] would measure each state independently. Basically I would try to identify whether the failure comes from retrieval or generation rather than treating the or generation rather than treating the whole system as one black box. So this

[21:41] is the exact way you can answer this question in your interview. I just tried to response in the way you are expected to respond. Now the next question the 23rd question of our series which is what is hybrid search? So hybrid search

[21:57] combines semantic vector search with traditional keyword based search. Vector search is good at matching meaning. Keyword search is useful when exact names, IDs, technical terms, numbers or product terms matter. For example, if

[22:14] someone searches for a 45823 payment failure, the incident ID may be extremely important, whereas pure semantic search may not always prioritize that exact identifier. Combining keyword and vector search

[22:28] often gives better results. So combining both of the search is what hybrid search is. Now the next question, what is reanking? So as a candidate, you can answer this question like this.

[22:41] Reranking is an additional step after retrieval. Suppose the vector database returns the top 20 potentially relevant chunks. Instead of giving all the 20 directly to the LLM, we can use another model to evaluate them more carefully

[22:57] and reorder them according to relevance. Then we might pass only the best five chunks to the LLM. Then can improve accuracy and reduce unnecessary context. So this is what basically reranking means. You can understand all these

[23:12] definitions and concepts very clearly because of these diagrams we have attached in our slides. These are more explanatory and I think these are helpful for you. You can take screenshot at any time. It is highly recommended

[23:26] because later you might forgot as human tendency. So the next question is what are AI agents? So an AI agent is an AI system that can do more than generate a

[23:38] response. It can understand a goal, decide what actions are required, use tools, observe results, and continue until the task is completed. For example, imagine a travel agent. Instead of only recommending hotels, an AI agent

[23:53] could potentially search hotel availability, compare prices, check a calendar, create an itinary, and interact with booking systems. The important difference is that the model is participating in a loop for

[24:07] reasoning, action, observation and further action. Now the next question, what is tool calling? So tool calling allows an LLM to interact with external functions or APIs. For example, if I search about what is the weather in

[24:23] Bangalore today, I don't want the model to just guess. The model can identify that it needs the weather tool. I receive the current date and then generate the answer based on that result. This makes LLM systems much more

[24:38] useful for real world applications. Now the next question, what is model quant? So as a candidate, you can answer this question very simply. So quantization reduces the numerical precision used to represent model weights. For example, a

[24:55] model might organically use 16 bit floating point weights. quantization could convert them to 8 bit or 4bit representation that can significantly reduce memory consumption and sometimes improve parent speed. The trade-off is

[25:10] that aggressively quantization may reduce model accuracy. [snorts] This is particularly useful when deploying models on GPUs with limited memory or even on edge devices. So the definition then examples and then the benefits and

[25:26] the minus point. The next question is what is LoRa? So start with its full form. So LoRa stands for low rank adaptation. It is a better efficient

[25:38] finetuning technique. Instead of modifying all the parameters of a huge language model, Lura introduces smaller transferable metrics into selected layers. That means we can customize a large language model while training only

[25:54] the relatively small numbers of additional parameters. This reduces training cost and memory requirements as well. Now the next question, how do you evaluate a generative AI application? The master question of our interviews.

[26:09] So as a candidate, you can answer the question like this. I would evaluate it across multiple dimensions rather than relying only one scope. First of all, on the accuracy, is the answer correct? Second, groundness. Is the answer

[26:25] supported by the provided information? Third, relevance. Does it answer the actual question? Fourth, retrieval quality. Did we retrieve the correct documents? Fifth, safety. Does it avoid harmful or restricted responses? Sixth,

[26:42] latency. How quickly does the system respond? And then the cost. How many tokens and resources are being consumed? And depending on the use case, we may also track user satisfaction or task completion rate. I would normally

[26:58] combine automated evaluation with human evaluation. So this is how you can answer this question in all the pointers. The perfect way to answer such kind of a question. Now the 30th question of our interview series, how do

[27:14] you prevent prompt injection? So as a candidate you can answer like I would treat prompt injection as a system level security problem rather than assuming one prompt can completely solve it. First system instructions should be

[27:27] separated from untrusted user inputs. External documents retrieved through rags should be treated as untrusted data. Then I would limit which tools the models can access and what permissions those tools have. For sensitive actions,

[27:42] I would use application level authorization and potentially require user confirmation. I would also validate to arguments sanitization inputs when appropriate log suspicious behavior and prevent sensitive information from being

[27:57] exposed. So security should exist outside the large model as well. So these were the top 10 most asked and expected questions in your advanced

[28:10] level generative AI interviews. Hold on, it's not enough. You can even expect scenario-based questions. Yes. So actually in this series we want you to prepare for all the levels. The beginner level, then intermediate level, then the

[28:24] advanced level. And that's not enough. We will even cover scenario based question. So now first let's look at the most basic and most common expected scenario based question in your generative AI interview. So now the case

[28:37] study one. So this case studies like building a company knowledge assistant. The interviewer might put a scenario in front of you. Then you have to answer on the based of the scenario. For example, interviewer put a scenario like this.

[28:52] Our company has around one lakh internal documents. Employees want to ask questions and receive answers based on those documents. So how would you build the system? So here the interviewer first giving you the scenario and

[29:06] remember the answer will always be from the concepts that we have covered. So you will answer this case study like this. I would probably start with a rag architecture. First I would collect the documents from approved sources. Then I

[29:22] would clean and parse them and break them into meaningful chunks. I would generate mings for those chunks and store them in a vector database along with metadata such as department, document type, access level, last

[29:36] updated date. When the employee asked the question, I would generate an for query and return to the most relevant chunks. I might also combine vector search with keyword search and reanking. Then I would provide those received

[29:52] chunks to the LLM and instruct it to answer only from the approved context. I would also return citation so employees can verify where the information came from. Finally, I would make sure the retrieval layer respects employee

[30:08] permissions. So someone from one department cannot access confidential documents belonging to the another department. This is how you explained it very thoroughly. Now let's understand the second kind of a case study which is

[30:22] a type of customer support chatbot. So here the interviewer might give you a scenario where he say we receive 50,000 customer support tickets every month.

[30:34] How would you use generative AI here? So as a candidate you can say like this. I would probably create a AI support assistant rather than immediately trying to completely replace human agents. The chatbot could use rag to access product

[30:49] documentations, FAQs, written policies, troubleshooting guides, and customer specific information where permissions are allowed. For simple requests such as, how do I reset my password? The chatbot could answer directly. If the

[31:05] customer says my account was charged twice, the system may need to call an internal billing API. For sensitive cases such as refund above a certain amount, legal complaints or unusual account activity, I would escalate the

[31:20] conversation to a human agent. I would also provide the human agent with an AI generated summary so they don't have to read the entire conversation. The important metrics would be resolution rate, escalation rate, accuracy,

[31:35] response time, customer satisfaction and hallucinine rate. So this is how you will solve this case study for your recruiter. Now not that's not enough. We

[31:47] will look at the third kind of a case study which is building an AI réumé screening system. Here the interviewer can put a scenario like this in front of can put a scenario like this in front of you. Suppose AR wants an AI system to

[32:00] analyze rumés against job descriptions. How would you approach it? So as a candidate you would say I would first define what AI is allowed to do. I would independently make final hiring

[32:14] decisions. Instead I would use it as decision support tool. The job descriptions and candidate rums could be converted into structured information such as technical skills, years of relevant experience, education with

[32:27] appropriate certifications and project experience. We could then calculate relevance based on predefined job requirements. An LLM could generate a summary explaining how the candidate matches those requirements. But I would

[32:42] exclude protected or irrelevant personal characters from the decision process. So these were some of the scenario-based questions you can expect in a generative AI interview. The key is not to just explain the concept but also show how

[32:59] would you apply them to solve real world problems. And that bring us to the end of our generative AI interview preparation. As you've seen the interview usually starts with the fundamentals LLM, transformers,

[33:12] embeddings, prompting and hallucinations. From there it moves into practical concepts like rag vector databases fine-tuning AI agents evaluation and optimization. At the advanced level however the interviewer

[33:26] do not just looking for the definitions they want to understand how would you approach the real world problems. So instead of only preparing for questions like what is rag be ready for the questions like your rag system is giving

[33:40] incorrect answers how would you identify the problem and improve it that is where scenario-based preparations really matters. Focus on understanding the concepts, the architecture behind them, the tradeoffs involved and how would you

[33:55] apply them in your real projects. The more clearly you can explain your approach and reasoning, the more confidently you will be able to handle even unexpected interview questions. So keep practicing, keep building real

[34:09] world generative AI projects and most importantly focus on understanding rather than memorizing. And with that, you are ready to take your generative AR interview preparation to the next level. All the best for your interviews and

[34:24] All the best for your interviews and I'll see you in the next session.