LLM and Generative AI Interview Questions

Preparation for an LLM or generative-AI role should cover fundamentals and application behavior. Google’s current Machine Learning Crash Course adds an LLM module on tokens, Transformers, prediction, architecture, and training. An OpenAI Research Engineer posting for Frontier Evals & Environments describes work on model capabilities, evaluation methodologies, continuous evaluation, training, and production systems. These sources illustrate topics relevant to particular roles and learning materials; they do not establish a universal interview checklist. At the application layer, candidates should be able to discuss how retrieval, prompting, fine-tuning, and tool use differ in purpose and constraints. A retrieval system can provide changing source material at request time, while fine-tuning changes model behavior through training; neither approach automatically guarantees factual answers. Evaluation should reflect the task, include representative and edge-case examples, and define measurable criteria. Anthropic’s public documentation recommends specific, measurable success criteria and task-specific test cases, including irrelevant, nonexistent, long, or ambiguous inputs. Production questions can involve latency, cost, reliability, privacy, safety, and quality tradeoffs. Explain what you would measure, how you would compare a baseline, and what failure should trigger a fallback or human review. For technical exercises, be ready to clarify assumptions, reason about data and model behavior, and explain how you would test a proposed change. Public job listings and interview guidance describe individual employers; use them to guide preparation without assuming every LLM role asks the same architecture or question.