How Retrieval-Augmented Generation (RAG) Works

Introduction

Retrieval-Augmented Generation (RAG) is a powerful paradigm in natural language processing (NLP) that combines the strengths of retrieval-based systems with generative models. By leveraging both approaches, RAG enables models to produce more accurate, contextually relevant, and informative responses, making it a vital component in tasks like question answering, summarization, and conversational AI.

RAG models operate in two main phases: the retrieval process and the generation process. Understanding these phases and their integration into an end-to-end workflow is crucial for appreciating how RAG enhances the capabilities of generative models.

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Retrieval Process: Query and Document Retrieval

The retrieval process is the first critical step in a RAG model. It involves selecting relevant pieces of information from a large corpus of data based on a given query. This step can be broken down into two main components: query processing and document retrieval.

Query Processing

When a user inputs a query, the model needs to understand the intent and context of the query to retrieve the most relevant information…