What is Dense Retrieval?
A retrieval method that uses dense vector embeddings, enabling semantic search and advanced contextual retrieval.
More about Dense Retrieval:
Dense Retrieval uses dense vector embeddings to match queries with documents based on semantic similarity, rather than relying on exact term matching. Models like bi-encoders and cross-encoders are often employed to create these embeddings.
Dense retrieval is a key component in systems like RAG and semantic search, providing superior accuracy in understanding user intent and delivering relevant results.
Frequently Asked Questions
What are the advantages of dense retrieval over sparse retrieval?
Dense retrieval captures semantic relationships between words, making it ideal for tasks like contextual retrieval.
What tools or models are commonly used for dense retrieval?
Popular tools include vector databases, powered by models like BERT and RoBERTa.
From the blog

How to Get Your Small Business Ready for AI
You keep hearing about Artificial Intelligence (AI) and wonder what itβs got to do with your business. The buzz is strong and it definitely sounds exciting, but is this big, must-go party exclusively for multibillion-dollar companies, or can small businesses get an invite, too?

Ane Guzman
Contributor

How to Train ChatGPT With Your Own Website Data
Training ChatGPT with your own data can provide the model with a better understanding of your unique context, allowing for more accurate and relevant responses.

Herman Schutte
Founder