Keeping large content repositories organized is an ongoing challenge. There’s always new stuff coming in, and taxonomies evolve over time. Resource-strapped teams seldom have opportunities to re-categorize older content.

It’s a task well-suited for generative AI. Large language models have powerful capabilities that can help teams keep content organized at scale. Using LLMs in this capacity can lead to better user experiences and free team members to focus on more valuable efforts.

This presentation explores two approaches for using LLMs to organize content at scale: 1) re-categorizing content using existing categories and 2) developing new categories from existing content. Both will be shown as proofs of concept alongside feasible next steps.