Retrieval-Augmented Generation (RAG) 101

Retrieval-Augmented Generation (RAG) bridges the gap between static LLM parameters and dynamic external data. In this guide, I break down the core components of a RAG pipeline—from document chunking and vector embeddings to context retrieval and generation—and explain how real-time tools like Web Search integration keep model answers accurate and grounded.

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