TL;DR
A highly precise retrieval paradigm where search queries and document tokens are encoded independently but interact through token-level similarity at search time.
Unlike traditional bi-encoders that compress entire texts into single vectors, late interaction stores contextualized embeddings for each token. During search, a maximum similarity operator compares query tokens directly to these token-level representations to compute document relevance scores. This preserves fine-grained linguistic nuances and structural information, resulting in substantial accuracy boosts for retrieval-augmented generation pipelines.
Why this matters for your business
It dramatically bridges the gap between speed and precision, making state-of-the-art token-level grounding feasible for production-scale vector search systems.
