Preeti
Retrieval-augmented generation (RAG) is transforming Natural Language Processing (NLP) by combining large language models with external knowledge sources, improving contextual understanding and response generation. The RAG pipeline involves leveraging external knowledge, using a retriever component to fetch relevant contexts, filtering and ranking retrieved information, and generating context-aware responses. Key steps in building a RAG pipeline include ingestion, chunking, embeddings, retrieval methods, and response generation/synthesis. Enhancements to RAG systems can be achieved through re-ranking retrieved results, the FLARE technique, and the HyDE approach.
RAG was introduced to help pre-trained language models access and utilize knowledge better, enhancing their performance in knowledge-intensive tasks. The RAG setup involves document retrieval using dense passage retrieval and response generation using models like BART. Training with RAG includes encoding questions into vectors, retrieving similar text chunks, and generating answers using BART. Two methods for combining outputs during inference are RAG-Sequence and RAG-Token. As technology advances, further refinements and innovations in the RAG pipeline will continue to improve AI applications across various domains.
Source link
Source link: https://medium.com/@ashleygreen9910/rag-pipeline-how-it-transforms-natural-language-processing-6ee7c076cc25?source=rss——ai-5
GIPHY App Key not set. Please check settings