What is ColBERT and where is it used?

ColBERT is a neural retrieval approach that represents queries and documents with multiple token-level embeddings rather than a single dense vector. This allows more precise matching because the model can compare important terms and concepts individually while still capturing semantic meaning. ColBERT and related late-interaction models are relevant to AI search because they show how retrieval systems can become better at finding passages that answer complex prompts. For content teams, the practical lesson is to write clear, specific passages with meaningful terms, entities, and context. Detailed, well-structured sections are easier for advanced retrieval systems to match and reuse.