two)
describes the role of embeddings in the RAG process?
C. Apply a MemoryChain that remembers past customer queries and uses this memory to answer future questions more accurately. D. Design a ParallelChain where multiple LLMs process different aspects of the customer query, such as order history and product details, combining them in the final answer. You are developing a document understanding system that integrates IBM watsonx.ai and Watson Discovery to extract insights from large sets of documents. The system needs to leverage watsonx.ai’s large language model to summarize documents and Watson Discovery to search and extract relevant data from those documents. What is the best approach to achieve this integration?
D. Use a higher temperature during the generation process You have completed a prompt -tuning experiment for a large language model (LLM) using IBM Watsonx, aimed at improving its ability to generate accurate responses to customer support queries. After the tuning process, you are analyzing the performance statistics of the model. Which statistical metric is the most appropriate to prioritize when evaluating the success of the prompt -tuning experiment?
You are working with a Watsonx Generative AI model to create marketing content that balances creativity with efficiency. The goal is to generate engaging content within a predefined time limit without compromising on quality. Given this context, which two optimization strategies will most effectively help you achieve both speed and content quality? (Select two)
prompt is tracked and accessible for rollback in case a newer version produces worse results. Which strategy would best ensure that all prompt versions are stored and easily retrievable, while minimizing disruption to the current deployment?
