Answer / Tarun Agarwal

A Large Language Model (LLM) is an artificial intelligence model designed to understand and generate human-like text. It works by learning patterns in a large dataset of text, allowing it to predict the next word or sentence in a sequence. This is achieved through a process called deep learning, where neural networks are trained to recognize complex relationships between words and phrases. The larger the dataset, the more sophisticated the model becomes.

What motivates you to work in the field of Generative AI?

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What is reinforcement learning with human feedback (RLHF), and how is it applied?

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What factors should be considered when comparing small and large language models?

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How do you prioritize tasks in a Generative AI project?

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What is semantic caching, and how is it used in LLMs?

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What are some real-world applications of Generative AI?

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

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

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What are some techniques to improve LLM performance for specific use cases?

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How do you measure diversity and coherence in text generated by LLMs?

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Why is security and governance critical when managing LLM applications?

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How can LLM hallucinations be identified and managed effectively?

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