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Understanding Retrieval-Augmented Generation (RAG) Architecture
In recent years, generative AI models have significantly progressed in text generation. However, they often suffer from limitations such as hallucinations and outdated knowledge. Retrieval-augmented generation (RAG) is an advanced AI architecture that enhances text generation by integrating real-time retrieval mechanisms. This article explores the RAG architecture, its components, advantages, and real-world applications.
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is an AI architecture that combines two key processes: retrieval and generation. Unlike traditional large language models (LLMs) that rely solely on pre-trained knowledge, RAG dynamically retrieves relevant external data before generating a response. This ensures more accurate, contextually relevant, and up-to-date answers.
Example: Before and After RAG
Before RAG:
User: “What are the latest advancements in quantum computing?”
Traditional LLM Response: “Quantum computing is advancing in areas such as qubit stability, quantum supremacy, and practical applications. Researchers are exploring ways to enhance quantum coherence and error correction.”
