RAG Explained: Retrieval-Augmented Generation in AI (Step-by-Step) скачать в хорошем качестве

RAG Explained: Retrieval-Augmented Generation in AI (Step-by-Step)

What RAG Means RAG stands for Retrieval-Augmented Generation. It’s a technique in AI and NLP that combines two powerful components: R → Retrieval The system searches and retrieves relevant information from external sources (like a database, knowledge base, or documents). Example: Instead of relying only on what the model “remembers,” it fetches fresh, factual data from a knowledge store. A → Augmented The retrieved information is then augmented (added) to the model’s context. This step enriches the model’s input so it can generate answers that are more accurate, grounded, and up-to-date. G → Generation Finally, the model uses the retrieved and augmented context to generate a response. This ensures the output is not just fluent but also factually supported by external knowledge.