What RAG (Retrieval-Augmented Generation) Meaning, Applications & Example
AI techniques that combine language models with information retrieval to generate more factual and coherent outputs.
What is RAG (Retrieval-Augmented Generation)?
RAG (Retrieval-Augmented Generation) is an AI framework that enhances text generation by combining information retrieval and generative models. It allows AI systems to retrieve relevant external information from sources like documents or databases before generating a response. This process helps create more contextually accurate and informed content by leveraging external knowledge in real time.
Types of RAG Models
- RAG-Token: Retrieves documents or passages related to a query and generates responses token by token, incorporating the retrieved information.
- RAG-Sequence: Retrieves documents and generates an entire response sequence based on the combined context from the retrieved information.
Applications of RAG
- Question Answering: Improves AI-based question-answering systems by retrieving relevant documents to provide accurate answers.
- Text Summarization: Enhances AI summarization tools by pulling in key data from different sources to create more complete summaries.
- Conversational AI : Powers chatbots by retrieving external data to generate responses tailored to real-time user queries.
Example of RAG
A virtual assistant using RAG could retrieve relevant information from product manuals or support articles to generate an accurate and helpful response to a customer’s inquiry, improving the assistant’s ability to provide context-aware, precise answers.