13 Common Large Language Model (LLM) Interview Questions and How To Prepare

Key takeaways

Potential interview questions for large language models include how to train them, use them across various applications, and make them more reliable.

• Other questions may cover topics such as LLM architecture, challenges in LLM training, and the role of attention mechanisms in LLMs.

• Be prepared for questions about the ethical implications of LLMs, including bias, data quality, data tagging, masking, and tokenization. 

• You can prepare a few questions to ask the interviewer at the end of your interview.

Prepare for your LLM interview by reviewing the following 13 interview questions. If you’re ready to sharpen your skills in this field, enroll in the Prompt Engineering Specialization from Vanderbilt University. In as little as four weeks, you can learn about large language modeling, data analysis, prompt patterns, responsible AI, and more.

13 common large language models interview questions

While questions will likely vary from interview to interview, practicing your answers to some common questions can be crucial preparation. 

1. What is a large language model (LLM)?

You are likely to get this elemental question in most, if not all, LLM interviews. Answer succinctly with something like this: 

• LLMs are artificial intelligence (AI) models developed to understand human language.

• LLMs allow computers to comprehend and replicate human language. 

• LLMs use machine learning (ML) to power popular interactive AI technologies such as ChatGPT. 

This last answer shows you know a bit about what people actually use LLMs for in the real world. It also implies you have some knowledge of ML, the key technology behind applications such as chatbots. 

Other forms this question might take: 

• What are the basics of LLMs? 

• Could you explain what an LLM is in plain language? 

2. How do you train LLMs?

Building on the knowledge of ML displayed above, you can discuss further how much you know about the components of LLM training. You can mention that programmers train LLMs with massive amounts of data. LLMs learn to predict outputs based on the likelihood of one word or phrase following another.

If the interviewer wants more detail, consider mentioning these three common training modalities: 

• Few-shot learning

• Fine-tuning

• Zero-shot learning

Read more: What Is Zero-Shot Learning?

The interviewer is looking to ensure you have the proper technical know-how for the job. But they aren’t necessarily just testing your technical prowess. They’re evaluating your communication skills—i.e., the extent to which you can explain complex concepts to a general audience.

Other forms this question might take: 

• How do you prefer to train LLMs? 

• What does your LLM training process look like? 

• How did you train LLMs in the past?

3. What are the key components of an LLM architecture?

The interviewer is asking this to gauge your familiarity with key LLM concepts such as: 

• Attention mechanism

• Data sets

• Deep learning

• Neural networks

• Transformer models

You can be confident here: These are key to LLMs, and you likely know this material well. 

Other forms this question might take:

• Can you tell me about the key components of LLM? 

• What are some components of LLM architecture?

4. How do LLMs generate text?

It might be helpful to use an analogy to answer this question: LLMs are something like giant autocorrect features, predicting the most likely next word or phrase based on patterns in the data they were trained on. They can be fine-tuned or retrained with updated data to improve their accuracy and relevance over time.

Let the interviewer know that you understand the applications of LLM text generation vis-à-vis the job for which you’re applying. Depending on what the company does, mention LLMs’ text generation capabilities in terms of: 

• Answering customer questions

• Language translation

• Text summarization

Other forms this question might take include: 

• What’s the method behind LLM text generation?

• How do LLMs generate different types of text? 

5. What are the challenges of training LLMs?

LLM training is highly sophisticated. An interviewer may want to know that you understand particular challenges inherent to LLM training. 

Mention that LLMs tend to be costly to develop and operate. They’re also highly complex and trained on so many data parameters that they can be vulnerable to: 

• Bias

• Inaccuracies

• Hallucinations

It’s important that you show your interviewer that you understand that, as valuable as LLMs are, they aren’t perfect. 

Other forms this question might take include: 

• What are some drawbacks of LLMs? 

• What risks come with the use of LLMs? 

• In what areas can programmers improve LLMs? 

Is an AI interview a red flag?

Although an AI interview may not inherently be a red flag, job candidates tend to prefer in-person interviews over automated ones for several reasons. One: Candidates may perceive AI interviews as impersonal and even disrespectful. Two: While the organization can acquire knowledge about you as a potential employee, you are unable to evaluate the atmosphere in which you might be working. Three: If you possess workplace skills such as leadership, adaptability, and creativity, conveying these qualities may be challenging during an AI interview.  

6. How can you use LLMs in various applications?

By this point in the interview, you’re discussing the real-world applications of LLMs rather than just how they work in theory. Mention key use cases such as: 

• Text classification

• Text completion

• Text generation

• Text summarization

• Text translation

It may be a good idea to discuss some key concepts of natural language processing (NLP) at this point. NLP is, after all, primarily responsible for the leap in language understanding LLMs can make. 

It’s also worth mentioning the value LLMs have in computer vision tasks—such as assisting with image captioning, image classification, and object detection—so that your interviewer knows that you understand the wide variety of use cases LLMs possess.

Other forms this question might take:

• What are some LLM use cases? 

• What can the average person use LLMs for? 

• What kind of language tasks are LLMs sophisticated enough for?

7. What is the role of attention mechanisms in LLMs?

Programmers train LLMs on enormous data sets that often naturally feature biased or noisy material. Your interviewer will want to know that you understand how to filter that out. 

One way of doing so, of course, is via attention mechanisms—an ML technique by which neural networks focus on the most important specific portions of input data. Knowledge of attention mechanisms shows an interviewer that you’re dedicated to discovering ways to improve LLM performance. 

Other forms this question might take: 

• Why do you use attention mechanisms in LLM training? 

• When would you introduce attention mechanisms in an LLM?

• What happens to an LLM in the absence of attention mechanisms? 

8. How can you fine-tune LLMs for specific tasks?

As you already discussed how to train LLMs, the question now is how to fine-tune an LLM after training. 

Focus on discussing how to fine-tune pre-trained LLMs to perform specific tasks. Discuss techniques such as: 

• Full fine-tuning

• Instruction fine-tuning

• Parameter-efficient fine-tuning

Make sure to mention task-specific fine-tuning, keeping the company’s specific domain in mind. This signals to your interviewer that you’re already thinking in a practical way about your day-to-day work as a prospective LLM employee. 

Other forms this question might take: 

• How do you prefer to fine-tune LLMs? 

• How would you fine-tune our company’s LLMs? 

• Is there a best way to fine-tune LLMs? 

9. What are the ethical implications of using LLMs?

Your interviewer wants to know that you are aware of the concerns many people have about LLMs and different forms of AI. 

This is an excellent opportunity to discuss the company's data governance strategy, including how it trains its LLMs, where it gets its data, and what it does to protect it. 

Remain upbeat while you and your interviewer talk about issues surrounding: 

• Bias

• Data quality

• Data tagging

• Masking

• Tokenization

Talking about the ethical issues associated with LLMs doesn’t show you’re a cynic; rather, it shows that you’re a conscientious technology professional. 

Other forms this question might take: 

• What are some objections to using LLMs? 

• What are your concerns regarding big data and LLMs? 

• Could you describe why someone might worry about using LLMs?

10. How can you make LLMs more robust and reliable?

Your interviewer wants to know that you’ve got LLM improvement in mind right from the start. Mention some common LLM optimization techniques, such as: 

• Choose an appropriate LLM framework.

• Continuously monitoring a model's output

• Define your evaluation metrics

• Fine-tuning for domain specificity 

• Leveraging retrieval-augmented generation (RAG) 

• Set up security guardrails

Your interviewer won’t expect you to set up an optimization program for their company’s specific LLM needs on the spot. They just want to know that you’re considering how you might do so in a general way. 

Other forms this question might take: 

• How can you improve an LLM?

• What can you do to maximize LLM reliability? 

• How can you pre-empt LLM difficulties? 

11. What is the future of LLMs?

This question is most likely used to gauge how well you’re following developments in the LLM field. 

You can mention ways in which LLMs may improve in the future. They may become: 

• Collaborative

• Customizable

• Easier to integrate with legacy systems

• Multilingual

• Secure

You might also mention that specific industries and sectors prioritize LLM usage and hope to see significant changes. These include the manufacturing and medical sectors.

Other forms this question might take: 

• How will LLMs change in the future? 

• How will LLMs change different industries in the future? 

• Will more people use LLMs in the future? 

12. How can you use LLMs to improve human-computer interaction?

Your interviewer wants to know that you’ve got a user-centric mindset regarding LLMs. Discuss how LLMs, with their increasingly advanced knowledge of human language, are available to non-specialists due to intuitive interface design and plain language comprehension. 

You can discuss LLMs’ role in creating better digital assistants, chatbots, and even self-driving cars, all of which have considerable potential for positive social impact. 

Other forms this question might take: 

• How can you make LLMs more user-friendly? 

• Do people interact better with computers with LLM capabilities?

13. What questions do you have for me?

Have a couple of questions in mind for your interviewer to let them know you’ve thought about the role, your place in the company, and how you feel about the company’s business goals. 

As much as anything, asking questions helps affirm your interest in the role. It also allows you to build a rapport with your interviewer, who may, after all, wind up being your co-worker or even immediate superior. 

Consider asking about how the company currently employs LLMs. Ask your interviewer how they’ve used them in the past, how they plan to implement them, and where you and your team fit into that process. 

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