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Introduction to Technical Questions

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A few years ago, a technical PM question meant system design: "Design the architecture for Instagram's Home Feed." Today, the new normal is technical questions about AI.

The expectation now covers hallucination mitigation, retrieval-augmented generation, token and latency tradeoffs, and fine-tuning versus synthetic data.

If you're an AI PM, you'd want to be fluent in these concepts. If you're a general PM, this fluency is a way to stand out. The tricky part is that this fluency is sometimes required, even if the job description doesn't say so.

What to expect

At many large tech companies, the standard PM loop has no dedicated technical round. Rounds cover product sense, strategy, and behavioral questions. Technical knowledge tends to come up inside those rounds.

Here's a sample of questions recently asked to PM candidates at top tech companies:

How technical do I really need to be?

It depends on the role. Look at the job description and the company's engineering footprint together, and let that tell you how deep to go.

The frontier AI labs and AI-native companies run the toughest technical bar. A few examples of what that looks like in practice:

  • NVIDIA expects you to defend every technical claim from your own background. Mention RAG or model quality in a past project story, and the follow-up will press for the actual metric, the specific tradeoff, and the system detail. As one principal-level PM candidate put it, "I would spend way less time on generic product sense prep and way more time making sure I can defend every technical claim."
  • Google DeepMind expects clean frameworks for offline versus online AI evals and a real opinion on RAG versus fine-tuning.
  • OpenAI has sent candidates an LLM research paper as a mini take-home and asked them to critique the fine-tuning strategy using only public information.
  • Sierra AI expects agent architecture cold: memory, RAG, MCP, quality controls, and eval metrics.

As AI takes over more of the day-to-day work, technical questions are showing up in companies that never used to ask them.

A Google L5 PM candidate who cleared the AI product round said it plainly: "If you are not actively using AI tools in your day-to-day, it is hard to fake the fluency because it shows up across product sense, execution, and technical."

What interviewers are looking for

Interviewers asking AI technical questions are assessing whether you can think like someone who builds and ships AI products. They're evaluating your ability to:

  • Use AI and systems concepts fluently and apply them correctly as the conversation gets specific
  • Understand how these systems fail
  • Reason through tradeoffs like RAG versus fine-tuning, latency versus quality, or cost versus accuracy, and commit to a recommendation rather than listing options
  • Connect technical decisions back to specific user outcomes
  • Go deeper when the interviewer pushes on a follow-up

A solid answer applies AI concepts to product problems when the question calls for it. You know the mitigations for hallucinations, understand when RAG makes sense, and can hold your own in a technical conversation without an engineer walking you through it.

A senior+ answer is grounded in decisions you've actually made. When follow-ups get specific (e.g., which embedding model, what your evals measured, why fine-tuning over RAG in that case), you answer from experience rather than falling back on a comparison table.

In this module, we'll break down every AI concept PMs are expected to understand and show you how to bring them into your answers naturally.

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