How Gen AI Knowledge is Tested in Interviews

Unless you’re interviewing for a machine learning engineer or AI researcher role directly involved in building models, interviews at AI companies like OpenAI, Anthropic, and Perplexity tend to test applied understanding, not deep mathematical or algorithmic knowledge.

That means you’re more likely to be asked "How would you make a model’s output more creative?" rather than "Explain how temperature affects model sampling."

And you might hear "What are the ethical risks of deploying an AI system in a high-stakes environment?" instead of "What are hallucinations in AI?"

At first glance, these questions seem easier. They use plain English and avoid jargon. But in reality, application questions are only simple on the surface.

To give thoughtful, credible, and feasible answers, you need a working understanding of how generative models actually function i.e. how they’re trained, what their limitations are, and what levers you can pull to influence output behavior.

Example question

Let’s compare how two candidates might answer this question:

"How do we mitigate the risks of deploying an LLM in high-stakes environments like healthcare or finance?"

❌ Candidate 1: Surface-level understanding

"For any high-stakes environment, we should train a custom model from scratch using domain-specific data.

There’s always a risk that the model gives wrong answers, so we should make sure users are warned and validate the information themselves.

At the end of the day, AI should only be an advisor; humans should make the final call."

What’s missing:

• Candidate didn’t use the term "hallucination" which is a keyword that the interviewer is likely looking for to assess fluency in this area.
• Training from scratch is impractical for most companies. Few have the data, compute, or expertise.
• The answer doesn’t identify why hallucinations happen or how to systematically reduce them.
• It relies on disclaimers instead of design-level mitigations.

✅ Candidate 2: Applied understanding

“The key risk here comes from model hallucinations i.e. when the LLM generates outputs that sound confident but are false or misleading.

In healthcare, that could look like a symptom checker giving inaccurate self-diagnoses. The goal is to reduce these hallucinations through careful design and deployment choices.

First, contextualize the model. Most hallucinations occur when the model lacks the necessary domain knowledge (a knowledge boundary problem). Instead of training from scratch, a more feasible approach is to fine-tune or use retrieval-augmented generation (RAG), where the model retrieves relevant medical documents or guidelines before generating outputs. Supplying the correct knowledge is key.

Second, adjust decoding parameters. For high-stakes use cases, lowering temperature reduces creativity and randomness, improving reliability.

Finally, incorporate humans in the loop where feasible. Human review can help validate or understand problematic outputs and guide iterative improvements to mitigate risk, though full validation of every output is typically impractical.”

Why this works:

• Focuses on the root causes (hallucinations, knowledge gaps).
• Highlights feasible technical mitigations (RAG, parameter tuning).
• Frames human involvement realistically as risk mitigation rather than a full solution.
• Avoids overstating prompt engineering as a guarantee of safety.

Conclusion

This example shows how having a deeper understanding helps you give more practical answers and avoid surface-level solutions.

Candidate 1’s response sounds reasonable, but it’s vague, impractical, and disconnected from how AI systems actually work. Candidate 2, on the other hand, identifies the real technical source of the ethical risk and proposes grounded, actionable solutions.

That’s exactly what interviewers are looking for.

They don’t expect you to build a model yourself. They expect you to understand how it behaves, where it can fail, and how to design responsibly around those limitations.

In other words, application-level questions still test for conceptual depth.

Without that depth, your answers will remain generic. With it, your reasoning becomes concrete, insightful, and credible, and you’ll stand out to interviewers.