most important when selecting the appropriate vector database for this application?
following steps is a prerequisite to generating effective vector embeddings from the unstructured text using an embedding API?
C. Leverage the user interface to create synthetic data that includes rare edge cases, such as technical support questions or multi -part inquiries. D. Simulate both simple and complex customer queries, including ambiguous or vague requests, in the synthetic data. E. Fine-tune the model immediately after generating synthetic data, without further inspection, to maintain efficiency. In the context of large -scale synthetic data generation for fine-tuning a generative AI model, which of the following practices can lead to data that effectively improves the model’s performance on downstream tasks?
C. Tuning Studio automatically deploys the fine-tuned model to production environments without requiring further testing. D. Tuning Studio provides real-time monitoring of model performance metrics during the fine-tuning process, allowing you to adjust hyperparameters effectively. When optimizing a generative AI model using the Tuning Studio in IBM Watsonx, which two of the following actions can most effectively improve model performance when dealing with underfitting issues? (Select two)
without needing large datasets. In the context of a Retrieval -Augmented Generation (RAG) system using IBM Watsonx, which of the following is the correct process for generating vector embeddings for document retrieval?
databases are designed for keyword -based retrieval. D. When storing highly structured relational data, as a vector database excels at managing tabular information efficiently. When using few-shot prompting, which of the following is a common limitation you may encounter while working with Watsonx AI for text generation tasks?
