AI & Technical question

Explain the challenges in training LLMs. How will you set up your team to achieve training objectives given the challenges you listed, on time and within budget?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Technical fluency on LLM training tradeoffs plus the PM skill of organizing a team against real constraints.

Name the core challenges: data quality and licensing, compute cost, evaluation difficulty, and alignment effort.
State which challenge is the biggest constraint given a fixed budget and timeline, e.g. compute cost.
Propose a team structure: a data-curation pod, a training/infra pod, and an eval/safety pod with clear owners.
Define milestones, e.g. data pipeline ready by week 4, first training run by week 8.
Describe how you'd trade off scope, such as a smaller model or fewer training tokens, if behind schedule.
Note how progress against budget would be tracked at each checkpoint.

Names concrete challenges: labeled data scarcity, compute cost scaling, hallucination and eval difficulty, alignment effort.
Ties team structure directly to the named challenges, not a generic org chart.
Gives a rough milestone cadence rather than an open-ended timeline.
Mentions using existing benchmarks plus a custom eval set for the target use case.

Talking only about model architecture and skipping the team, budget and timeline management ask.
Giving a vague team structure with no ownership boundaries.

Chapters of the AI PM course that teach what this question tests.