Text Splitters in n8n Explained | Chunking for RAG, Embeddings & AI Workflows

Description

Text splitters are a critical building block for AI workflows in n8n, especially when working with RAG (Retrieval-Augmented Generation), embeddings, and large documents like PDFs. In this video, we cover: What text splitters are and why they are required How chunking helps LLMs handle large documents Different types of text splitters in n8n Character vs Recursive vs Token-based splitters Best practices for chunk size and overlap Where to place text splitters in n8n RAG workflows Common mistakes that break embeddings and retrieval accuracy If you’re building AI agents, chat with PDF workflows, or vector database pipelines in n8n, this video will help you design scalable and accurate AI systems. Perfect for: n8n beginners and advanced users AI workflow builders RAG and vector database practitioners Developers integrating LLMs with documents 👍 Like | 💬 Comment | 🔔 Subscribe for more AI + n8n content