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

Generative AI Interview Questions

Questions Answers Views Company eMail

What are some real-world applications of Generative AI?

What metrics do you use to evaluate the performance of a fine-tuned model?

What are the best practices for deploying Generative AI models in production?

How do you optimize LLMs for low-latency applications?

What challenges arise when scaling LLMs for large-scale usage?

How do you integrate Generative AI models with existing enterprise systems?

What is the role of containerization and orchestration in deploying LLMs?

What metrics are used to evaluate the quality of generative outputs?

How do you measure diversity and coherence in text generated by LLMs?

What is perplexity, and how does it relate to LLM performance?

How do you evaluate the impact of model updates on downstream applications?

How do foundation models support Generative AI systems?

What techniques can improve inference speed for LLMs?

How do you identify and mitigate bias in Generative AI models?

What is hallucination in LLMs, and how can it be controlled?

Un-Answered Questions { Generative AI }

What are the key steps involved in deploying LLM applications into containers?

How is Generative AI transforming the AI landscape?

How do Generative AI models create synthetic data?

How do you incorporate user feedback into Generative AI systems?

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What is Generative AI, and how does it differ from traditional AI models?

Explain the importance of tokenization in LLMs.

Which developer tools and frameworks are most commonly used with LLMs?

What metrics do you use to evaluate the performance of a fine-tuned model?

How can Generative AI contribute to scientific research?

How is Generative AI used in healthcare?

Why is data quality critical in Generative AI projects?

How do you balance innovation with practical business constraints?

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

Can you explain reinforcement learning and its role in improving LLMs?

What are the key elements to consider when creating user interfaces for LLM applications?

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