U2xAI
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AI PM Role and Fundamentals

AI and LLM Basics Every PM Needs Interview Questions

The technical baseline interviewers expect you to explain simply: prompting versus RAG versus fine-tuning, system prompts, context windows and the other terms that come up in every AI product discussion. Useful for anyone in a non-engineering AI role.

  • 21 questions with model answers
  • 10 foundational · 11 intermediate

Foundational AI and LLM Basics Every PM Needs Questions

  1. Explain what a token is and why a PM should care about it.Concept
  2. Describe the difference between a context window and a model's memory.Concept
  3. What is the practical difference between prompting, RAG and fine-tuning for a product decision?Concept
  4. Explain hallucination in one paragraph a sales team could repeat accurately.Interview
  5. What does temperature control and when would you lower it in a product?Concept
  6. Describe what an embedding is and one product feature it makes possible.Concept
  7. Explain the difference between latency and throughput and which one your users feel.Concept
  8. What does it mean for a model to be multimodal and what product surfaces does that unlock?Concept
  9. Explain the tradeoff between a larger model and a smaller one in plain product terms.Concept
  10. I will name five technical terms. Explain each to me as if I were your CEO.Interview

Intermediate AI and LLM Basics Every PM Needs Questions

  1. What is a system prompt and why is it a product asset rather than a code detail?Concept
  2. Explain what function calling lets a model do that plain text generation does not.Concept
  3. Describe what an eval is and how it differs from a unit test.Concept
  4. What is quantization and why might it show up in a cost conversation?Concept
  5. Explain why the same prompt can cost different amounts on different days.Concept
  6. What is prompt caching and what product decision does it enable?Concept
  7. Describe what a reasoning model does differently and when the extra cost is worth it.Concept
  8. Explain the difference between training data and retrieval data to a compliance officer.Interview
  9. What does model deprecation mean for a shipped product and what should you have in place before it happens?Case
  10. Explain what an agent is in a way that distinguishes it from a chatbot with tools.Concept
  11. A PM says the model needs more training on our data. Diagnose what they probably actually need.Case