Dual Encoder

Definitions

1. A retrieval architecture that encodes queries and documents separately for fast vector similarity search. 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: Dual Encoder 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 Dual Encoder 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

• RAG
• Vector Database
• Context Window
• Retrieval-Augmented Generation
• Vector Database
• Attention
• Approximate Nearest Neighbor
• Dense Retrieval
• Matryoshka Embeddings
• Retrieval Augmented Generation