This RAG Trick Makes Your AI Agents WAY More Accurate (n8n)

Uploaded: 2025-10-13
[“This video discusses the limitations of RAG (Retrieval-Augmented Generation) agents when they lack contextual understanding of document structures, leading to inaccuracies in responses.”,”The presenter explores various context expansion techniques to enhance the information retrieval process using N8N, including neighbor, parent, and full document expansions, while emphasizing the importance of maintaining document hierarchies for more comprehensive answers.”]

channel	The AI Automators
top	Engagement_month

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Common Pitfalls: RAG agents often miss the bigger picture because they don’t account for document structures, leading to incomplete or misleading results.
Solution Overview: The video discusses various context expansion strategies like neighbor, parent, and agentic expansions to improve RAG agent accuracy.
Implementation: Demonstrates how context expansion can be implemented in n8n, using tools like Superbase for document hierarchy retrieval and AI for query refinement.
Technical Details: Explains chunking methodologies, including smart markdown chunking, enabling nuanced document navigation while minimizing LLM usage.
Scaling Strategies: Highlights scalability solutions like contextual snippets and direct vector store injection to handle large datasets efficiently.

Quotes:

The biggest reason rag agents fail is that they can’t see the big picture.

This is how hallucinations happen.

You don’t necessarily need a knowledge graph to map all of this.