ColBERT (Contextualized Late Interaction over BERT) represents each document as a set of per-token embeddings rather than a single vector. At query time, it computes a MaxSim operation — matching each query token to its closest document token — enabling fine-grained relevance scoring while still allowing document embeddings to be pre-computed and indexed. This late-interaction approach achieves cross-encoder-level quality with much better latency.