The provided code is designed to evaluate the performance of a language model in assigning urgency and sentiment categories to customer emails by utilizing few-shot learning within SAP's Generative AI Hub.
1. Few-Shot Learning in Prompt Engineering:
* Definition:Few-shot learning involves providing a language model with a limited number of examples to enable it to perform a specific task effectively. In this context, the model isgiven a few examples of categorized messages to learn how to assign urgency and sentiment to new, unseen emails.
2. Code Functionality:
* Prompt Template Creation:The prompt_test variable defines a template that instructs the model to extract and categorize messages, specifying the desired output format as a JSON string.
* Example Selection:The code randomly selects a subset of examples from a development set (dev_set) to include in the prompt, demonstrating the expected input-output pairs to the model.
* Model Interaction:The function f_test sends the constructed prompt, along with the input message, to the language model for processing.
* Response Handling:The model's response is expected to be a JSON string containing the assigned urgency, sentiment, and categories for the input message.
3. Purpose of the Code:
* Performance Evaluation:By using few-shot learning, the code evaluates how well the language model can generalize from the provided examples to accurately categorize new customer emails. This approach assesses the model's ability to understand and apply the categorization criteria based on minimal training data.