| A few examples: * My company has a very large codebase, and I am not familiar with 99% of it. I can use an AI assistant like Sourcegraph Cody to explain parts of the codebase to me at a high level, and suggest areas where I should dive in to address my specific problem. * If I am working with a language, library, or framework I have not used before, I can ask ChatGPT to explain how a certain function works, and provide some code examples. If I write some code an get unexpected results, I can paste my code and results into ChatGPT, and ask it to tell me what went wrong. * Someone sends me a spreadsheet that specifies some business logic, and I want to transform that spreadsheet into a YAML file, and write some code to parse the YAML config and take some action based on user-supplied data. ChatGPT is pretty good at this. In all of these cases, I have to take the AI output with a grain of salt, and may have to do some supplementary research or debugging. But that's also the case when I ask coworkers for help. Right now, I would say generative AI provides a small boost to my productivity, but I can see that boost growing larger as language models improve. |