Dense Retrieval

Definitions

1. A search approach that retrieves items by comparing learned dense embeddings rather than sparse term overlap. It influences how models are trained, evaluated, or served, and it can materially change accuracy, robustness, latency, cost, or interpretability. Practitioners usually track it alongside data quality, compute limits, and validation results when moving models into production.

   In plain English: Dense Retrieval is an AI concept that affects how a model learns, predicts, or gets deployed. It matters because it changes quality, speed, or reliability.

   Example: "We revisited Dense Retrieval during model evaluation because the first run looked fine offline but behaved poorly in production, and the adjustment improved quality without breaking our latency or compute budget."

Related Terms

• Vector Database
• Vector Database
• Semantic Search
• Approximate Nearest Neighbor
• Dual Encoder
• Matryoshka Embeddings
• Retrieval Augmented Generation
• AI Search
• Neural Search