What does RAG Stand For
RAG stands for Retrieval-Augmented Generation. Here's a brief article on the topic:
Retrieval-Augmented Generation (RAG) is an advanced technique in natural language processing that combines the power of large language models with external knowledge retrieval. This approach aims to enhance the accuracy and reliability of AI-generated responses by grounding them in up-to-date, factual information.
In a RAG system, when a query is received, relevant information is first retrieved from a knowledge base or external sources. This retrieved information is then used to augment the input to a language model, providing context and factual grounding for the generation process. The result is a response that leverages both the language model's ability to understand and generate human-like text and the accuracy of retrieved information.
RAG offers several advantages:
- Improved accuracy: By incorporating external knowledge, RAG reduces the likelihood of generating false or outdated information.
- Adaptability: The knowledge base can be updated independently of the language model, allowing for more current responses.
- Transparency: RAG systems can often provide sources for the information used in generating responses.
As AI continues to evolve, RAG represents an important step towards more reliable and informative AI-assisted communication and decision-making.