Top 30 Generative AI and LLM Interview Questions
The most frequently asked questions on this topic across all roles and companies, ranked by real interview frequency. Updated weekly.
Explain how to evaluate a generative model using offline and online methods, with attention to hallucination, product metrics, and experiment design.
Delta Electronics Americas
Samsara
CNAExplain LLM hallucination and give three practical ways to reduce it using grounding, prompting, and evaluation.
ClickUp
Meta
AnthropicDesign a grounded document Q&A system and explain how vector search improves retrieval quality, latency, and hallucination control in RAG.
SCAN Health PlanMeta
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Explain a practical approach to fine-tuning an LLM for a specific task, including data, evaluation, and hallucination risks.
Ernst & Young
Atlas Copco Group
DecagonDesign a grounded multi-agent assistant that plans, retrieves, and synthesizes answers under strict latency, cost, and hallucination limits.
Jpmorgan Chase &
Vanderbilt University
FujitsuReduce hallucinations in a RAG system even when retrieval is already correct, using grounding, verification, and evaluation.
Invoca
Leidos
Boston Consulting GroupCompare RAG and fine-tuning, and decide when each is the better fit for an LLM product.
OpenAI
PRICE WATERHOUSE COOPERS
Capgemini Government SolutionsDiscuss how you designed an LLM system for a business use case, including evaluation, hallucination control, and cost latency tradeoffs.
Apple
Klaviyo
Invisible Agency