You are responsible for deploying an LLM in a customer service application. The LLM should effectively manage and prioritize different types of customer queries, such as urgent complaints, general inquiries, and feedback. However, during testing, you observe that the model struggles to prioritize urgent complaints appropriately, treating them with the same priority as general inquiries. The senior team member suggests adjusting the training process to improve the model's ability to handle priorities. Which strategy is most likely to improve the model’s ability to prioritize urgent complaints?

You are training a Large Language Model (LLM) to generate customer service responses. During evaluation, you notice that the model is generating polite but factually incorrect responses to customer queries. Which approach should you take to address this issue?

You are developing a machine learning model for predictive maintenance in a manufacturing plant. The model needs to analyze sensor data from thousands of machines in real-time, identifying patterns that indicate potential failures. This requires processing a continuous stream of data, some of which exceeds the available memory capacity. Which approach is most suitable for handling this complex scenario?

When using iterative prompt engineering to refine the behavior of a generative AI model, which approach is most effective in aligning the model’s output with your specific intentions?

You are working on a project that involves generating product descriptions for a large e-commerce platform. The platform sells a wide range of products, from electronics to clothing. The generative AI model currently in use produces high-quality descriptions for electronics but struggles with fashion items, often generating vague or inaccurate content. What is the most effective strategy to improve the model's performance across all product categories?