Retrieval-Augmented Generation (RAG) is an innovative approach in natural language processing (NLP) that combines the strengths of retrieval-based and generation-based models. Traditional generation models, like GPT-3, create text based solely on the input they receive. However, this can lead to inaccuracies, especially when the model needs more specific knowledge. RAG enhances this by retrieving relevant documents or data from an external source and incorporating this information into the generated output. This improves accuracy and allows for more contextually relevant and informative content.
https://businesscompassllc.com/selecting-the-right-technologies-for-implementing-rag-with-amazon-bedrock/)
