embedding maxxing

The practice of optimizing every possible dimension of your vector embedding pipeline to extract maximum performance — tuning model choice, chunk sizes, overlap, similarity thresholds, and indexing strategies until the system is running at its theoretical limit. Embedding maxxing is a rabbit hole: there's always another parameter to tweak, another reranking layer to add, another embedding model to benchmark. Engineers who go deep on this tend to surface weeks later looking slightly wild-eyed but with dramatically improved retrieval metrics.