You are evaluating two different generative AI models for a financial forecasting tool. The goal is to determine which model provides more accurate and actionable forecasts based on historical data. An initial A/B test shows both models perform similarly, with only minor differences in accuracy. You need more definitive results to make a decision. Which approach would be most effective in refining your evaluation to distinguish between the two models?

You are tasked with fine-tuning a pre-trained Large Language Model (LLM) for a customer service chatbot. The chatbot must handle a wide variety of customer inquiries with high accuracy while being sensitive to variations in language use across different regions. Which approach would best meet these requirements?

When dealing with a large dataset that exceeds the memory capacity of a single GPU, which approach is most effective for reading and processing the data using cuDF and Dask cuDF?

You are building a generative AI application that needs to process and understand large volumes of text data, identify entities, and store semantic embeddings for similarity searches. Which combination of tools would best support this task?

You are experimenting with two different generative AI models for summarizing legal documents. To determine which model performs better, you decide to compare them using statistical performance metrics. Which of the following metrics and methods should you prioritize for a meaningful comparison? (Select two)