What is Retrieval-Augmented Generation? What components does it consist of?

Retrieval-Augmented Generation (RAG) is an approach in natural language processing that combines generative models with a mechanism for searching relevant information from external sources.

Main components of RAG:

1. Retriever — responsible for searching and extracting relevant documents or fragments from a knowledge base or data corpus based on a query.

2. Generator — usually a large language model (LLM) that generates a response or text based on the obtained documents.

3. Integration — a mechanism that combines search results with generation, allowing the model to use the found information for a more accurate and justified response.

This approach improves the quality of responses, especially when the generative model does not possess all the necessary information or when data relevance is required.