Answer-first summary for fast verification
Answer: Provide detailed examples of desired summaries in the prompts.
Providing detailed examples of desired summaries in the prompts helps the model understand the specific format and content requirements. This technique, known as few-shot learning, guides the model to generate more accurate and concise summaries.
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In a scenario where you need to improve the responses of an Azure OpenAI model for a legal document summarization task, what prompt engineering techniques would you apply to ensure the model generates accurate and concise summaries? Describe the steps you would take to refine the prompts.
A
Use generic prompts without specifying the type of document.
B
Provide detailed examples of desired summaries in the prompts.
C
Use very short prompts to save processing time.
D
Randomly vary the prompts to test different responses.
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