What is Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is a concept in the context of large language models (LLMs) that allows for more specific and relevant responses. LLMs are powerful but rely on general training data, which may not be domain-specific or up-to-date. RAG addresses this by constraining the LLM to only consider information from specific data sources provided in the prompt. This ensures that the generated content is contextual, accurate, and relevant to the specific data sets.