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Explain the concepts of pretraining and fine-tuning in LLMs.
How do you ensure collaboration between data scientists and software engineers?
What are some best practices for crafting effective prompts?
How do generative adversarial networks (GANs) work?
How do you prevent overfitting during fine-tuning?
What is reinforcement learning with human feedback (RLHF), and how is it applied?
What are the differences between encoder-only, decoder-only, and encoder-decoder architectures?
How do you balance innovation with practical business constraints?
Can you describe a challenging Generative AI project you worked on?
How can the costs of LLM inference and deployment be calculated and optimized?
What is semantic caching, and how does it improve LLM app performance?
Can you explain reinforcement learning and its role in improving LLMs?
What are some real-world applications of Generative AI?
How can organizations identify business problems suitable for Generative AI?
How does a cloud data platform help in managing Gen AI projects?
