following best summarizes the value of using reusable prompts in this context?
(Select two)
B. An untrained model with a minimal number of layers to reduce complexity. C. A model trained from scratch on a small, specific dataset. D. A pre -trained model with very few layers to ensure fast processing speeds. You’ve conducted a prompt -tuning experiment, and after reviewing the generated outputs, you observe issues such as incomplete responses, irrelevant content, and occasional factual inaccuracies. What is the most appropriate action to address these data quality problems?
enabling the discovery of relevant code even when the query and code do not use the same keywords. D. It ensures that the code snippets returned are exact matches to the keywords in the query, avoiding irrelevant code. You are using IBM watsonx’s generative AI model to generate responses for a chatbot, and you want to ensure that the model stops generating text when it encounters a specific phrase like “End of Response.” Which of the following settings for stop sequences is most appropriate to achieve this goal?
embeddings. You are tasked with fine-tuning a pre-trained large language model (LLM) using synthetic data generated through the IBM watsonx user interface. Which of the following steps should you follow to ensure the model is fine -tuned correctly and the synthetic data is used effectively?
You are working with a Generative AI model to generate a summary of a large financial report. To reduce costs, you are exploring different model parameters such as minimum and maximum token limits. Which configuration would help minimize generation costs while ensuring an accurate summary of the document?
