{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Dense Retrieval — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">Dense Retrieval</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">A search approach that represents both queries and documents as dense vectors, embeddings, and finds relevant results by measuring similarity in that vector space. Unlike keyword search, dense retrieval captures semantic meaning rather than exact word matches, making it much better at finding relevant content even when the wording differs between the query and the document.  See also: embedding, vector database, retrieval-augmented generation.</p><a href=\"https://gaks.ai/glossary/dense-retrieval\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/dense-retrieval →</a></div>"}