Retrieval Augmented Generation (RAG) is a pattern that works with pretrained Large Language Models (LLM) and your own data to generate responses.
RAG models offer several advantages over traditional language processing models:
- Improved contextuality: By incorporating retrieval, RAG models can generate responses that are more contextually relevant and informed.
- Enhanced accuracy: The ability to retrieve information from external sources helps ensure that the generated responses are accurate and up-to-date.
- Knowledge enrichment: RAG models can tap into a vast knowledge base, allowing them to provide more detailed and informative responses.
