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Learn to build effective RAG (Retrieval-Augmented Generation) agents in n8n by mastering four key approaches to handling retrieval and context. Discover why traditional chunk-based retrieval often leads to hallucinations and inaccurate responses due to missing context, then explore practical alternatives used in real-world systems. Master using filters to narrow context effectively, implement SQL queries to provide full structured context to your agents, understand when to use complete context approaches, and apply vector search for semantic matching scenarios. Gain insights into when each method works best, their limitations, and how to choose the right approach for specific use cases in your AI automation projects.
