Dense-Retrieval

• Retrieval-Augmented Generation

  Grounding language model generation in retrieved external documents; reduces hallucination and enables knowledge updates without retraining.

• Hybrid Search

  Combining dense vector similarity and sparse term-matching scores to balance semantic understanding with keyword precision.

• ColBERT

  Contextualized Late Interaction over BERT; late-interaction ranking using per-token embeddings with MaxSim scoring for efficient dense retrieval.

• Bi-Encoder

  Neural architecture encoding query and document independently into separate embeddings, enabling fast retrieval via approximate nearest-neighbour search.

• Approximate Nearest Neighbour

  Fast nearest-neighbour search algorithm sacrificing exactness for speed; enables practical dense retrieval at scale. Abbreviated ANN.

• Comparing BM25 and Dense Retrieval for a Product Catalogue

  A side-by-side evaluation of keyword search and embedding-based search on a realistic product dataset, showing where each approach wins and how hybrid search splits the difference.