Dense retrieval is a technique designed to find relevant documents by using vector representations, typically learned through machine learning models, rather than relying solely on keyword matching. Unlike traditional sparse retrieval which uses inverted indexes and term frequency, dense retrieval employs pre-trained or fine-tuned embeddings to measure semantic similarities between queries and documents. This approach is particularly useful for capturing context and meaning, making it effective in applications like question answering systems.