Retrieval Augmented Generation (RAG) is a technique in the field of natural language processing (NLP) that aims to improve the quality of generated texts by using external data sources. It combines two central approaches:
- Retrieval: This refers to the retrieval of relevant information or text from a large knowledge database or document repository. This information can serve as the basis or context for subsequent text generation.
- Generation: This step uses a generative language model (like GPT) that generates new text based on the retrieved information.
How RAG works
- Retrieval: First, a query or prompt is sent to a retrieval module. This module searches a database or knowledge base for the most relevant documents or passages that match the query.
- Integration: The retrieved information is then made available to the generative model. The model can use this information to generate the text, leading to a contextually richer and more accurate result.
- Generation: Finally, the language model generates the text, taking into account the information retrieved.
Advantages of RAG
- Improved accuracy: By retrieving relevant information, the generative model can provide more precise and factually correct answers.
- Extended knowledge base: The model can access a much larger knowledge base than would be possible through its own training alone.
- Dynamic responses: Since the retrieved information is used in real time, the model can respond better to current or specific requests.
Application of RAG
RAG is often used in areas where accurate and contextual information is required, such as answering questions, creating technical documentation, or supporting customer service requests. It is also used to cross-check generated content with reliable sources to increase trustworthiness.
Overall, RAG is a powerful approach to significantly extend the capabilities of generative models by integrating external knowledge.