email and various timestamp formats) and example values that Akita has observed in use, so ChatGPT had much more to work with. Our OpenAPI3 specs feature additional data that we infer, such as string formats (e.g. My initial luck with ChatGPT led me to feed it an entire OpenAPI3 endpoint spec generated by Akita. So one day I tried feeding it some basic information about our internal APIs, and was surprised to see ChatGPT read between the lines and describe (with reasonable accuracy) the likely purpose of each endpoint using only an HTTP method and a URL. While I was impressed, the assistant’s tendency to extrapolate data in unpredictable directions and outright make stuff up made me doubtful of its suitability as a genuinely useful tool for technical work.īut as the Founding Frontend Engineer at Akita, I always wish we could be doing more to present the interesting data we already have-and ChatGPT’s potential to help with this is exciting. When ChatGPT first came out, I spent most of my time with it asking it to pretend to be various historical figures or to write bad poetry. Talk to Aki today, no code changes or SDKs necessary. In this blog post, I’ll talk about the motivation for our new friend, show what he can do, and talk about some of the limitations people will need to watch out for with alpha-Aki. I’m excited to introduce to the world Aki: our ChatGPT-powered assistant. It turns out that, by using ChatGPT in our web console, we could get surprisingly great results at all of those tasks. At Akita, our main value proposition is helping users find and fix production issues as quickly as possible, by telling them about their APIs while requiring as little work as possible.īut our users often asked for features we hadn’t built yet, including the ability to automatically generate documentation, write tests for APIs, and provide example API request and response payloads.
0 kommentar(er)
