Canonicalizing LLM Labels with Embeddings and DSU
318
Use embeddings + vector search + DSU clustering to canonicalize LLM-generated labels, yielding consistent, cheaper, and faster classification at scale.
Why Embeddings Got Bigger—and Where Efficiency Pulls Them Next
113
Embeddings got bigger with Transformers and APIs, but new efficiency techniques and infrastructure mean the future is about smarter—not just larger—dimensions.
The Dimensional Ceiling of Single-Vector Embedding Retrieval
151
Embedding-based retrieval hits a hard top-k capacity ceiling set by embedding dimension, and real systems already run into it.