Answer / Rajesh Jha

Reinforcement Learning (RL) is a type of machine learning that enables an agent to learn by interacting with an environment. In the context of LLMs, RL can be employed to train models that make decisions based on reward signals by optimizing policies that select appropriate actions in specific situations. This can lead to better performance and adaptability in diverse applications.

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

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How can data governance be centralized in an LLM ecosystem?

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What are the benefits and challenges of fine-tuning a pre-trained model?

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How do you manage context across multiple turns in conversational AI?

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How would you design a domain-specific 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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Describe the Transformer architecture used in modern LLMs.

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What techniques are used for handling noisy or incomplete data?

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How do foundation models support Generative AI systems?

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What considerations are involved in processing for inference in LLMs?

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What are the advantages of combining retrieval-based and generative models?

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