You are working with a senior team member to deploy a large language model (LLM) across a multi-cloud environment. The senior team member asks you to assist in evaluating the model's scalability. The model is expected to handle a significant increase in traffic as the user base grows, and you need to identify any potential bottlenecks. What is the most appropriate first step in evaluating the model's scalability in this scenario?
You are working with a Large Language Model (LLM) trained on general-purpose data, but now you need it to perform well on legal document processing. After fine-tuning the model on a legal dataset, it still struggles with accurately interpreting certain legal terminologies and produces inconsistent outputs. What would be the most effective next step to improve the model's performance?
A production system running a generative AI model is experiencing memory leaks, leading to gradual system slowdowns and crashes. Which of the following strategies would most effectively help in diagnosing and resolving this issue?
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)
You are working on a generative AI project that requires training a large language model (LLM) on a dataset containing millions of customer reviews. However, the dataset includes many reviews with misspellings, redundant information, and irrelevant content. What would be the most appropriate preprocessing step to handle this issue?