Answer / Beeram Singh

Some key terms and concepts that are important to understand when working with Language Models (LLMs) include:
1. Corpus: A collection of text data used for training an LLM.
2. Tokenization: The process of breaking down text into individual tokens or words.
3. Embedding: A representation of a token as a high-dimensional vector in a continuous space.
4. Transformer architecture: A neural network architecture commonly used for building LLMs, designed to handle long sequences of data efficiently.
5. Fine-tuning: The process of further training an existing pre-trained model on a specific task or dataset.

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

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How do you decide whether to fine-tune or train a model from scratch?

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How do few-shot and zero-shot learning influence prompt engineering?

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Can you provide examples of how to structure prompts for a given use case?

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What is perplexity, and how does it relate to LLM performance?

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How do you ensure knowledge sharing within your team?

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How do you optimize LLMs for low-latency applications?

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

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What are the best practices for integrating LLM apps with existing data?

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What distinguishes general-purpose LLMs from task-specific and domain-specific LLMs?

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How do you ensure collaboration between data scientists and software engineers?

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How do you prioritize tasks in a Generative AI project?

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