Hybrid Retrieval is an information retrieval approach that combines sparse retrieval methods, such as BM25 or TF-IDF, with dense retrieval methods based on neural embeddings, to improve the accuracy and coverage of search results. Sparse methods excel at exact keyword matching, while dense methods capture semantic similarity between queries and documents. By merging ranked results from both approaches, hybrid retrieval systems achieve broader recall and greater precision, and are widely used in retrieval-augmented generation pipelines and enterprise search applications.
Most retrieval-augmented generation systems stop at documents, ignoring the relational databases that power finance, healthcare, and research. Our researchers built a rule-driven framework that learns which source to query for each question, delivering better answers at lower computational cost.