使用模型和部署模型名称：
- Model name: text-davinci-003
- Deployment name: text-davinci
In the Completions page, ensure your text-davinci deployment is selected and then in the Examples list, select Classify text.
Replace all of the text in the prompt area with the following text:
比如通过一段文章
You are a teacher creating a test for your students.
Write three multiple choice questions based on the following text.
Most computer vision solutions are based on machine learning models that can be applied to visual input from cameras, videos, or images.
- Image classification involves training a machine learning model to classify images based on their contents. For example, in a traffic monitoring solution you might use an image classification model to classify images based on the type of vehicle they contain, such as taxis, buses, cyclists, and so on.
- Object detection machine learning models are trained to classify individual objects within an image, and identify their location with a bounding box. For example, a traffic monitoring solution might use object detection to identify the location of different classes of vehicle.
- Semantic segmentation is an advanced machine learning technique in which individual pixels in the image are classified according to the object to which they belong. For example, a traffic monitoring solution might overlay traffic images with “mask” layers to highlight different vehicles using specific colors.
设置相关的参数：
the Parameters pane, set the following parameter values:
- Temperature: 0
- Max length (tokens): 500
- Pre-response text: Auto-generated questions. Validate before using in a test:
第一次响应如下：
Change the Temperature parameter value to 0.9 and then use the Regenerate button to regenerate the response.
进行了相关参数修改后，第二次响应如下：
全文参考：