What is the overall goal of the custom prompt evaluation workflow described?	To build a system that evaluates how well a prompt performs when generating code specific to AWS use cases.
What are the three required output types that the prompt must generate when responding to a user task?	Python, JSON configuration, or a raw regular expression.
What is the primary constraint on the prompt's output format?	The output must contain only the requested code/configuration, with no explanations, headers, footers, or extra text.
What is the required structure of the input data set used for prompt evaluation?	An array of JSON objects, where each object contains a `task` property describing the required task.
How can a dataset be assembled for the purpose of prompt evaluation?	It can be assembled manually or automatically by generating it using Claude.
Which model is suggested for generating the dataset to improve efficiency?	A faster model like Haiku.
What specific method is used when calling Claude inside the `generate data set` function?	The pre-filling and stop sequence method.
What is the defined stop sequence used in the API call within the `generate data set` function?	``` ```
What is the final step taken after generating the dataset to prepare it for evaluation?	Saving the list of tasks to a file named `dataset.json` using `json.dump` with an indent of two.