包阅导读总结
1. 关键词：AI prompts、Gemini、Workspace、Experiment、Share
2. 总结：文本介绍了在 Workspace 侧面板为 Gemini 写更好的 AI 提示的 5 种方法，包括具体示例、不断尝试优化、不同角色实验、分享成功与不足等，以提升效果和产品的帮助性。
3. 主要内容：
– 以规划外地活动为例，说明如何向 Gemini 提问获取酒店信息。
– 强调通过实验和迭代来改进提示，包括使用同义词、调整细节和特异性、测试不同长度等。
– 推荐尝试使用不同角色，对比结果并记住有效的方式。
– 提到开发 Gemini 时团队通过群聊分享提示和响应示例，获取实用任务成功经验，持续改进并根据反馈升级模型，强调更多输入能使产品更有用。
思维导图：
文章地址：https://blog.google/products/workspace/google-gemini-workspace-ai-prompt-tips/
文章来源：blog.google
作者：Molly McHugh-Johnson
发布时间：2024/7/29 19:15
语言：英文
总字数：1013字
预计阅读时间：5分钟
评分：78分
标签：Gemini,Google 工作空间,AI 提示,生产力,跨产品集成
以下为原文内容
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For example, let’s say you’re planning an offsite and get an email from a team member asking for the hotel information so they can book a room. Ask Gemini in Gmail to look it up from a Google Doc that contains all the offsite details with: “what is the hotel name and sales manager email listed in @Company Offsite 2024.” Then, you can easily insert the information into your email reply.
4. Experiment and iterate to get better results
If at first you don’t succeed, try, try again! Refine your prompt and experiment with different approaches, including using synonyms or different keywords, adjusting the level of detail and specificity and testing different prompt lengths. (Based on the team’s research, the most successful prompts average around 21 words, yet people’s initial attempts are significantly shorter — usually fewer than nine words.) Plus, don’t forget you can ask follow-up questions.
Vishnu recommends experimenting with using different personas, too. For example, when you’re writing a prompt about training someone, you may want to ask Gemini to act as a colleague and then compare the results against asking Gemini to act as a teacher.
“When you’re happy with the results, take note of what’s working,” Vishnu says. “Try reusing some of that language with future prompts.”
5. Share what’s working — and what could work better
When development began on Gemini for Workspace tools, the team started a group chat where they shared examples of their prompts and Gemini’s responses with each other.
“The chat provided insights into the types of tasks people are using gen AI for, highlighting both its capabilities and limitations,” Vishnu says. “Googlers found success with practical tasks like summarizing itineraries and extracting information from documents.” As we’ve continued to upgrade our models, we’ve also been able to address previous areas of feedback, like how Gemini can contextualize information across multiple sources.
These are the kinds of insights the team is still gathering, both from users inside Google and from trusted testers. It all helps. As Vishnu puts it, “The more input we get, the more helpful we can make our products.”
