1. 案例一:市场分析
2. 案例二:健康咨询
3. 案例三:教育资源编制
本文探讨了基于第一性原理的Prompt设计方法,旨在深度融合系统信息与用户信息,以提升AI交互的精准度和实用性。通过挖掘系统信息(如AI模型的逻辑结构、知识库和处理能力)和用户信息(包括需求、目标和偏好),我们可以设计出更具个性化的Prompt,确保生成的内容能够准确回应用户的需求。此外,借鉴《思考,快与慢》的思维模式,我们可以在Prompt设计中实现“快思维”与“慢思维”的有机结合,从而在速度和深度上实现平衡。这种多角度的设计思路不仅能够提升AI的响应效果,还为未来的Prompt设计打开了更多创新的可能性。在面临挑战的同时,Prompt设计的广阔机遇也逐渐显现:它不仅能够增强AI在各个领域的应用能力,还能推动个性化服务的深入发展,最终让AI与用户的交互更加贴心、高效。
gpt_prompt = """{"instructions": {"style": "Friendly and informative", "clarity": "The text should be easy to understand for a general audience", "adaptability": "The text should be adjustable to different tones if requested", "specificity": "Use specific examples to make abstract concepts clearer"}, "purpose": "The prompt aims to guide GPT in generating content that is not only accurate but also easy to engage with, supporting both entertainment and education goals.", "structure": {"introduction": "Start by introducing the core topic in a concise way, making it interesting to capture attention.", "main_body": {"sections": ["Background Information - Provide context about the topic to ground the discussion.", "Details and Examples - Give specific examples that help to clarify the subject.", "Counterpoints - Briefly mention any alternative views or common misconceptions."], "formatting": "Use bullet points or numbered lists for readability if content is complex."}, "conclusion": "Summarize key points clearly, restate why this information matters to the audience, and add a call to action if relevant."}, "length": {"min": 200, "max": 400}, "engagement_features": ["rhetorical_questions", "emotional_language", "interesting_facts"], "language_features": {"verbosity": "medium", "complexity": "The language should be formal yet accessible; avoid jargon unless explained."}}"""; print(gpt_prompt)