Intro to PromptQL's Continuous Learning Layer

In a world where AI is confidently wrong, where it answers fast but misses the nuances of your business, PromptQL feels different. So what makes it different? The answer is a continuous learning loop, one that hones the craft of analysis for your business just like your best analyst. Chronicle's continuous learning loop really comes down to three superpowers. First, when it's asked a question, it doesn't just answer. It shows you how confident it is and flags what it doesn't know. Second, it invites users and experts to step in, to give it feedback, to teach it. And third, it absorbs those lessons, continuously improving from the collective knowledge of your users. This is how prompt cure captures tribal knowledge, all those insights and nuances that usually live in people's heads and turns them into shared reusable intelligence. We believe that this is the only way to truly scale accuracy in enterprise AI. Most companies rely on a semantic layer for accuracy, and this layer is usually maintained by a handful of data custodians or data guardians, and we believe that model of maintaining accuracy just does not scale. PromptQL changes that you're effectively crowdsourcing the teaching and the learning, but with guard rails. And when you see this learning loop in action, it feels magical. Too good to be true is something we often hear. So how does this learning loop work? In this 3-part series, we'll break down the innovation behind the magic.