Retrieval-Augmented Generation (RAG) is a powerful technique that combines a strong existing language model with a retrieval system to efficiently handle company-specific information. Unlike retraining models, which is often impractical, RAG leverages a vector-based search to fetch relevant company documents and uses a language model to generate answers. This approach involves a retriever for searching and a generator for response crafting, significantly improving efficiency. Advanced techniques like RAG Fusion, Cross and Bi-Encoders, and ensemble retrievers enhance the system's accuracy and relevance. Tuning methods such as RELP and FLARE further optimize model performance, making RAG an effective solution for handling unstructured data and varying queries.
Sort: