How do you approach working with incomplete or ambiguous requirements?
How do you approach learning a new AI framework or technology?
How can the costs of LLM inference and deployment be calculated and optimized?
What is the importance of attention mechanisms in LLMs?
What are some techniques to improve LLM performance for specific use cases?
What does "accelerating AI functions" mean, and why is it important?
What are the challenges of using large datasets in LLM training?
How do you balance transparency and performance in Generative AI systems?
How do you ensure Generative AI outputs comply with copyright laws?
Can you describe a challenging Generative AI project you worked on?
What is the role of vector embeddings in Generative AI?
What metrics do you use to evaluate the performance of a fine-tuned model?
How do foundation models support Generative AI systems?
How do you ensure knowledge sharing within your team?
How do you ensure collaboration between data scientists and software engineers?
