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What is prompt engineering, and why is it important for Generative AI models?
How do you design prompts for generating specific outputs?
What are some best practices for crafting effective prompts?
How do few-shot and zero-shot learning influence prompt engineering?
Can you explain the difference between discriminative and generative models?
Can you provide examples of how to structure prompts for a given use case?
Why is data quality critical in Generative AI projects?
How do you prepare and clean data for training a generative model?
What techniques are used for handling noisy or incomplete data?
Explain the importance of tokenization in LLMs.
What is the role of vector embeddings in Generative AI?
What are the benefits and challenges of fine-tuning a pre-trained model?
How do you decide whether to fine-tune or train a model from scratch?
What is reinforcement learning with human feedback (RLHF), and how is it applied?
How do you prevent overfitting during fine-tuning?
