kapa.ai reposted this
"My estimate is that the average AI answer saves a developer 15-30 minutes of work, which is insane." 🧠 The smart folks at monday.com embedded kapa.ai as an AI chatbot directly into their developer portal. See Daniel Hai's awesome post below for for more details.
AI Chatbots are Raising the Bar for API Developer Experience 📈🤖 At monday.com, we recently launched our AI developer assistant in our documentation and API playground. My conclusion? Integrating an AI support bot may be one of the simplest, yet most effective ways to improve the developer experience of your API. Here are my few cents on the transformative impact AI chatbots can have for API products: ⏰ Time Saved: My estimate is that the average AI answer saves a developer 15-30 minutes of work, which is insane. (Finding the right docs and connecting the dots) 📚 Boosting Documentation: The issues and gaps in your API documentation become crystal clear when you see answers the AI could not answer, or answered wrong. 🎯 Prioritization: Basic classification allows you to identify the frequency of various questions, thereby highlighting areas that require more attention in the documentation and API. 💰 Impressive ROI: You only need to fix your knowledge source once, and from now on, the bot will always answer the issue correctly. Now that's some great ROI! 🔟 The 10X Rule: The AI bot receives a significantly higher volume of questions compared to human support, as developers often prefer to find answers independently and not ask what they feel might be “stupid” questions or wait for a technical support rep to get back to them. 🚀 Real-time Experience: Engineers who are looking at your API right now might move on or churn by the time they hear back from your support or a community. The ability to get answers in real-time is a huge benefit to keeping them engaged. 🌍 Multilingual: I was surprised to discover the range of languages in which users interacted with the bot - even more impressive was the bot's ability to respond in each of these languages. This one totally caught me by surprise. 📊 Data: In addition to query analysis, the use of AI models' resources to answer questions through embeddings can also be monitored. Reading conversations with the bot feels strikingly comparable to observing user sessions on tools like HotJar or FullStory, I feel like I can understand how the developer thinks and approaches the problems. 🎯 Context: At monday.com, we embedded the AI bot within our developer center, minimizing context switches and boosting productivity. 💻 Interface: An AI chatbot extends beyond being just a tool - it’s an interface with numerous potential features that can enhance the user experience. For example, our bot suggests questions and provides resource links for answered queries, in addition to suggesting further points of reference. While there are other advantages, those listed above are the most significant for me. At monday.com, we were able to quickly launch a developer-specialized AI assistant 🤖 by partnering with kapa.ai. Go and check them out! 🚀 Do these insights align with your perspective? 🧐 #api #chatbot #developerexperience #AI