Next-generation content management platforms use generative AI to help content teams work beneath and beyond the traditional “page” — but only if you can reliably tell them how your content is connected in meaningful, repeatable ways. Fortunately, you already have the tools to build the model your new platform needs.
Generative AI offers some enticing shortcuts for tagging large content sets. But even outputs that "look" right might not meet user goals—and may not be any more effective than what can be generated with time-tested natural language processing approaches. This article describes an experiment that compares these two methods to discover the essential ingredients for an effective tagging strategy.
Developing a taxonomy to meet the business needs of your content collection is a crucial step toward regaining control of your content ecosystem. Too often, however, well-intentioned taxonomy solutions never fully make it to the systems they're meant to serve. Don't let this be your fate! Here's the process I use to migrate taxonomy drafts in a spreadsheet to the taxonomy management tool in Sanity CMS.
By situating data in context, knowledge graphs create valuable opportunities for content creation, recommendation, and management—even at small scales. A boutique approach to knowledge graph design offers a scalable strategy for starting out small and solving well-defined communication and discovery problems to create measurable value.
Voice user interfaces, smart software agents, and AI-powered search are changing the way users—and computers—interact with content. Whether or not you’re building services for these emerging technologies, structured content is now necessary to ensure the accuracy and integrity of your content across the evolving digital landscape.