We often get asked what the difference is between using highlight tags for clustering or using clusters. In this article we'll try to clear the air.

Clusters are designed to help you nurture and (automatically) enrich big ticket items, such as assumptions, hypotheses, insights, or other UX artifacts or product deliverables. Clusters can be given a name, description and labels and contain one or multiple highlights. Other than tagged highlights, clusters form a single package that typically represents "something new" and can be shared at once.

Tags for highlights are also a form of mental modeling or "clustering". However, people typically use tags to let them find back highlights afterwards, so that they can combine selected highlights into clusters manually.

In short, highlight tags are often used for an "initial clustering", making it easy to find back highlights afterwards and crafting clusters from them. Clusters, on the other hand, are designed for nurturing and organizing big-ticket items that you want to enrich with evidence over time – almost like your research backlog.