What strategies can be used to adapt LLMs to a specific use case?
What are the challenges of using large datasets in LLM training?
What are the privacy implications of using large datasets for Generative AI?
Why is specialized hardware important for LLM applications, and how can it be allocated effectively?
How can latency be reduced in LLM-based applications?
What metrics do you use to evaluate the performance of a fine-tuned model?
What are the key differences between GPT, BERT, and other LLMs?
What motivates you to work in the field of Generative AI?
How can data governance be centralized in an LLM ecosystem?
What metrics are used to evaluate the quality of generative outputs?
How do you prevent overfitting during fine-tuning?
What are the best practices for integrating LLM apps with existing data?
What strategies can alleviate biases in LLM outputs?
How can LLM hallucinations be identified and managed effectively?
What distinguishes general-purpose LLMs from task-specific and domain-specific LLMs?