Post by yamanhosen5657 on Mar 7, 2024 0:39:42 GMT -5
Zapier said he used both ChatGPT and GitHub Copilot to handle rote tasks, like asking documentation questions, suggesting improvements to existing code, and creating boilerplate code for certain situations. An engineer asking ChatGPT for code But he said that, in a weird way, using AI to code has made him more confident and comfortable as a programmer: it's helped him overcome the malaise of writing boilerplate copy, while also allowing him to focus on the parts of engineering that truly excite and matter to him—the parts that require human interaction. I also spoke to Deb, one of Zapier's blog editors, who used ChatGPT to see if it could help her write email copy. But the editing involved ended up taking her significantly longer than had she just written the copy herself.
Despite this, Deb's experience of refining the prompts she provided to the AI turned out to be an exercise in learning more about how AI works, and also prompted her to reflect on the core objectives of the newsletter she was writing. Was the goal to succinctly summarize articles? Or was it to offer fresh insights? Or something else entirely? By asking Panama mobile number list AI to do your actual job for you, your teammates will hopefully realize that humans are (usually) better than it—but they'll also start to see its benefits and figure out how they can use it in other aspects of their role. 4. Frame AI as a draft-maker, not a decision-maker I wouldn't trust AI to write and send an email on my behalf without first checking it—and that applies to any sort of output the AI gives. You should make this very clear to your team: AI should not be used without human oversight.
And not just because of ethical and legal concerns—also because, at the end of the day, AI lacks contextual understanding. Take our Events team at Zapier. They receive a lot of emails from people who have questions about specific events they're hosting. The team decided to experiment with AI to help them manage that workload. They set up a workflow where the AI takes support questions from incoming emails, then runs them through a list of common questions and answers to see if there's a match. If it finds a match to the question, the AI will draft a written email response the events team can check—and they can decide whether they'll send it.
Despite this, Deb's experience of refining the prompts she provided to the AI turned out to be an exercise in learning more about how AI works, and also prompted her to reflect on the core objectives of the newsletter she was writing. Was the goal to succinctly summarize articles? Or was it to offer fresh insights? Or something else entirely? By asking Panama mobile number list AI to do your actual job for you, your teammates will hopefully realize that humans are (usually) better than it—but they'll also start to see its benefits and figure out how they can use it in other aspects of their role. 4. Frame AI as a draft-maker, not a decision-maker I wouldn't trust AI to write and send an email on my behalf without first checking it—and that applies to any sort of output the AI gives. You should make this very clear to your team: AI should not be used without human oversight.
And not just because of ethical and legal concerns—also because, at the end of the day, AI lacks contextual understanding. Take our Events team at Zapier. They receive a lot of emails from people who have questions about specific events they're hosting. The team decided to experiment with AI to help them manage that workload. They set up a workflow where the AI takes support questions from incoming emails, then runs them through a list of common questions and answers to see if there's a match. If it finds a match to the question, the AI will draft a written email response the events team can check—and they can decide whether they'll send it.