Answer / Mahesh Kumar Gupta

Using open-source LLMs comes with risks such as poor quality data, lack of transparency, and potential biases in the training data. To mitigate these risks, it's essential to carefully evaluate the source of the model, inspect the training data for bias, and implement techniques like fairness-aware training.

How do you select the right model architecture for a specific Generative AI application?

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

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What steps are involved in defining the use case and scope of an LLM project?

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

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

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What are the key steps in building a chatbot using LLMs?

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How do you prevent overfitting during fine-tuning?

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

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What are diffusion models, and how do they differ from GANs?

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Can you describe a challenging Generative AI project you worked on?

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

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

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