Improve your Azure OpenAI customer support chatbot! Discover prompt engineering techniques for accurate, context-aware responses. Ace the AI-102 exam and enhance your career!
Table of Contents
Question
Your organization, Xerigon Corporation, has developed a customer support chatbot powered by Azure OpenAI. The chatbot must provide accurate and context-aware responses to user queries about the company’s products and services. To improve the quality of the chatbot’s responses, you are educating users on how to apply prompt engineering techniques.
Which of the following is NOT true about writing more effective prompts? (Choose all that apply.)
A. Set the context for the model to guide its response.
B. Do not use section markers in prompts.
C. Provide examples of the desired response format.
D. Do not use cues when prompting the model for code generation.
E. Use clear and specific instructions in the prompt.
Answer
B. Do not use section markers in prompts.
D. Do not use cues when prompting the model for code generation.
Explanation
Avoiding section markers can make prompts harder to interpret, reducing the effectiveness of the response. Therefore, this statement is false as using section markers is beneficial. For example, headings or labels can improve the clarity and organization of prompts, especially when dealing with complex instructions or multiple tasks. By explicitly guiding the model, section markers enhance its ability to produce accurate and structured responses.
Not using cues reduces the model’s ability to generate accurate code. Including cues is a recommended practice, making this statement false. Cues, such as specific programming language names or desired output formats, help guide the model when generating code. For instance, including “Write a Python function” in the prompt ensures that the model provides output in the correct language and structure.
Using clear and specific instructions helps the model understand exactly what is expected.
Providing examples of the desired response format helps the model understand the structure and style of the expected output. This technique, known as few-shot prompting, allows the model to mimic the provided format, improving the accuracy and consistency of its responses. Providing examples is the best way to guide the model, making this an essential practice for prompt engineering.
Setting the context ensures that the model understands the specific scenario or background information needed to generate relevant responses. This can include details about the topic, the target audience, or the purpose of the output. Contextual prompts help the model focus on generating responses aligned with the user’s intent.
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