The RAG framework is an innovative approach in natural language processing (NLP) that combines the strengths of retrieval-based and generation-based models. In RAG, the system first retrieves relevant information from a large database (vector store) using a retrieval mechanism.
This retrieved information is then used to augment the generation process, where the model generates responses or outputs based on both the retrieved information and the input query or context.
This framework enhances the contextual understanding of the model by leveraging existing knowledge from the retrieved data, leading to more accurate and coherent responses. RAG is particularly effective in tasks where access to relevant context is crucial for generating high-quality outputs.
The quality of the entire process, which encompasses both the retrieval and generation phases, is monitored through the measurement of a set of key performance indicators.
