Answer / Ravi Kumar

The challenges of using large datasets in Large Language Model (LLM) training include excessive computational resources, longer training times, data privacy issues, potential biases in the data that may lead to prejudiced responses from the model, and the need for efficient methods to handle unstructured or noisy data.

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

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

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What is the most innovative Generative AI project you have contributed to?

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What role will Generative AI play in autonomous systems?

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What is text retrieval augmentation, and why is it important?

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Explain the importance of tokenization in LLMs.

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

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

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How do you prepare and clean data for training a generative model?

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

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What are the differences between encoder-only, decoder-only, and encoder-decoder architectures?

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How can data pipelines be adapted for LLM applications?

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