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.
Capgemini
Pratt & Whitney
Cisco Restaurant + BarExplain LLM hallucination and give three practical ways to reduce it using grounding, prompting, and evaluation.
ClickUp
Meta
ActianDesign 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
Decagon
Hewlett Packard EnterpriseDesign a grounded multi-agent assistant that plans, retrieves, and synthesizes answers under strict latency, cost, and hallucination limits.
Jpmorgan Chase &
Vanderbilt University
OM GroupReduce hallucinations in a RAG system even when retrieval is already correct, using grounding, verification, and evaluation.
Leidos
Next Ventures
Boston Consulting GroupCompare RAG and fine-tuning, and decide when each is the better fit for an LLM product.
OpenAIEEverforth CyberCoders
Capital OneDiscuss how you designed an LLM system for a business use case, including evaluation, hallucination control, and cost latency tradeoffs.
University of Texas Permian Basin
The Boston Consulting Group
Continental General