Clustering unstructured text with LLM embeddings and HDBSCAN

You've got a pile of unstructured text — customer reviews, support tickets, social posts — and you need to figure out what's in it without reading every line yourself. The trick now is to let an LLM turn each chunk into a vector, then cluster those vectors with HDBSCAN.

HDBSCAN finds clusters of varying density and automatically labels outliers as noise. Unlike KMeans, you don't have to guess how many groups exist. Feed in a few thousand sentences, watch the algorithm carve them into natural groups, then ask an LLM to name each cluster. The result is a labeled taxonomy you didn't have to build by hand.

Why this matters for us: Brown and Black communities produce mountains of unstructured data — community reports, patient notes, small business reviews — and tools that can cluster and label it automatically mean less gatekeeping and more say over what gets counted.