Answer / Nitin Kumar Sharma

To decide between fine-tuning or training a model from scratch, consider these factors: 1. Availability of pre-trained models for the task at hand; 2. Quality and quantity of available labeled data; 3. Computational resources required for training from scratch versus fine-tuning; 4. Desired level of personalization for the specific application.

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

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Can you explain the difference between discriminative and generative models?

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How can organizations identify business problems suitable for Generative AI?

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What is prompt engineering, and why is it important for Generative AI models?

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What are prompt engineering techniques, and how can they improve LLM outputs?

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Explain the importance of tokenization in LLMs.

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How do you ensure Generative AI outputs comply with copyright laws?

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

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

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What factors should be considered when selecting a data platform for Generative AI?

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How is Generative AI applied in music composition?

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

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