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

• 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.