第141章 智能语言模型

ChatGPT Can Give Great Answers. But Only If You Know How to Ask the Right Question

ChatGPT能给出很棒的答案，但前提是你知道如何以正确的方式提问

By Jackie Snow

With the new generative AI tools， like ChatGPT， anyone can put in a prompt—type in a few words and get a result. But that doesn't mean that the results will be relevant or accurate. The key is putting in the right prompt.

有了像ChatGPT这样的新型生成式AI工具，任何人都可以向系统输入提示词——只需寥寥数语，就能得到一个结果。但这并不意味着你得到的结果就会是切题的或准确的。关键是要输入合适的提示词。

That's why some businesses are now hiring prompt engineers—people with the skills， background and practice to talk to so-called large-language AI systems effectively—and why many people expect prompt engineering to be a desirable skill for many kinds of workers to add to their résumés.

这就是为什么一些企业正在招聘提示词工程师：这些人要具有相关技能、背景和实践经验，可以有效地与所谓的大型语言人工智能系统对话；同时，这也解释了为什么很多人预计，多个行业的求职者都会想要把“提示词编写能力”添加到自己的简历中。

In general， the more detailed the request， the closer the response will e to what the user is looking for. But the most effective prompt engineering goes beyond adding detail. Adding the right detail to get the best response is crucial. Each tool also has particular capabilities and limitations that users can learn to manipulate.

一般来说，输入的要求越详细，得到的回答就越接近于用户想要的东西。但编写提示词最有效的方式不仅是添加细节；要获得理想回答，关键是要添加恰当的细节。而且，每种工具也都有自身特定的功能和局限性，用户可以学着去操控它们。

Even for people applying to jobs that don't explicitly mention prompt engineering， having this skill could bee essential for all sorts of roles.

即使求职者申请的岗位中没有明确要求提示词编写能力，但拥有这项技能可能将是完成各种工作任务的必要条件。

Some of the prompt methods that researchers and users have discovered include telling a large-language model “Let's think step by step，“ a method that gets the technology to break plicated requests into smaller steps that are easier to address. Another technique involves telling the AI that it is an expert in a field and its job is to give advice to a layperson. That can help simplify the response on plicated subjects.

研究人员和用户已经找到了一些提示词编写方法，比如，告诉一个大型语言模型“让我们一步步地思考”，这种方法可以运用技术将复杂的请求分解成更容易处理的小步骤。还有一种方式是告诉人工智能它是某一领域的专家，它的工作就是向外行人提供建议，这就有助于简化对复杂问题的回答。