Generative AI Interview Questions, Answers for Freshers and Experienced asked in various Company Job Interviews

Generative AI Interview Questions

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What is Generative AI, and why is it significant in modern enterprises?

Can you explain the historical context of Generative AI and how it has evolved?

What are Large Language Models (LLMs), and how do they relate to foundation models?

How is Generative AI transforming the AI landscape?

What does "accelerating AI functions" mean, and why is it important?

Why is data considered crucial in AI projects?

How can Generative AI create value for enterprises?

What are pretrained models, and how do they work?

What are the trade-offs between security and ease of use in Gen AI applications?

How does a cloud data platform help in managing Gen AI projects?

How can LLMs be categorized?

What distinguishes general-purpose LLMs from task-specific and domain-specific LLMs?

Can you explain the key technologies and principles behind LLMs?

What are vector embeddings, and why are they important in LLMs?

What key terms and concepts should one understand when working with LLMs?

Un-Answered Questions { Generative AI }

What is a vector database, and how is it used in LLM applications?

What techniques are used for handling noisy or incomplete data?

How do you handle conflicts in an AI team?

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Why is data quality critical in Generative AI projects?

How do you stay updated with the latest research in Generative AI?

What strategies can simplify LLM development and deployment?

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How can Generative AI create value for enterprises?

What strategies can be used to adapt LLMs to a specific use case?

How does learning from context enhance the performance of LLMs?

Can you explain the difference between discriminative and generative models?

What is the role of Generative AI in gaming and virtual environments?

How do AI agents function in orchestration, and why are they significant for LLM apps?

How do you balance transparency and performance in Generative AI systems?

What are some techniques to improve LLM performance for specific use cases?

How does transfer learning play a role in training LLMs?

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