Extract Meaning From Unstructured Text Into Existing Fields You already have the fields. What you don’t have is time to fill them. Incoming text—emails, PDFs, product descriptions, order notes—is written for humans, not databases. Some of it maps cleanly to your schema. Most of it doesn’t. The real work is interpreting what matters and ignoring the rest. In this class, you’ll see how to take a large, unstructured block of text and let AI extract only the usable information—then populate the relevant existing fields in FileMaker. Fields that don’t apply are left untouched. Nothing is inferred where it shouldn’t be. No schema changes. No re-architecture. No pretending the data is cleaner than it is. This is a small, production-safe pattern for developers who already know their data model—and want AI to handle interpretation, not decision-making. lI'd argue that every database needs a way to show what matters to users on the home screen / Main Menu / Dashboard. Sometimes that’s a full set of graphical widgets. Sometimes it’s just a single badge or small widget showing a number, a trend, or a warning meant to drive action. This screen should no longer just be a bunch of buttons. In FileMaker, these widgets are usually hand-built: you decide what to measure, how to display it, and how users can interact with it. I’ve built a lot of them over the years. They work—but only for the moment they’re built. Then the questions change, the data shifts, and small widgets quietly turn into brittle, high-maintenance code. This class shows a different approach, starting small—with a single badge or widget. Instead of hard-coding how a widget should behave, you’ll learn how to teach it what the data represents and what it’s meant to support, and then let AI decide how that information should be presented. The same weekly invoice data, for example, might show a trend one day, call out an unusually large invoice another day, or switch to a warning when overdue invoices appear—all without adding rules, switches, or rewrites. The approach is intentionally safe and practical: Your existing FileMaker scripts still gather and validate the data AI handles only interpretation and presentation The result is something you can prototype quickly and ship confidently This isn’t about experimenting with AI. It’s about adopting a pattern—one that fits naturally into real FileMaker solutions and gets better over time without adding complexity. You’ll leave with a clear mental model, a working example, and a strong sense of where this belongs in production—and where it doesn’t. If you’ve been building widgets the same way for years—or haven’t built them yet—this class shows a better place to start.