I built an automation that fixes the single biggest weakness in most retrieval-augmented generation systems. The problem is simple: agents retrieve isolated fragments of documents, but they have no idea where those fragments sit in the document structure. That missing structure strips away the meaning those fragments need. I call the solution context expansion. It … Read more

Why agents need a map of your data I build AI agents all the time. They are great at language and reasoning. What they often lack is context about how pieces of data relate to each other. An agent can answer questions about a single record. It struggles when the answer depends on connections across … Read more

I built and tested many Retrieval-Augmented Generation (RAG) systems over hundreds of hours. What I learned is simple: there is no single best way to build RAG. The right design depends on what you need your system to do, how fast it must respond, how much accuracy you require, and what resources you can afford. … Read more

I spent two days building and testing Gemini File Search inside an n8n workflow. Lots of people called it a game changer that will kill traditional RAG systems. After hands-on work I found five critical aspects most people are missing. Some can quietly break a production system if you assume everything is taken care of … Read more

I created a video that walks through how I built a dynamic hybrid RAG (Retrieval-Augmented Generation) search engine in n8n, and this article captures that same approach in detail. My goal here is to explain why vectors alone often fail, how different retrieval methods work, and how you can combine them so an AI agent … Read more

I created an automation that imports documents into a Supabase vector store so an AI agent can query them. The system worked fine for small knowledge bases, but it fell apart when I tried to scale it. After about 100 hours of tuning and testing, I reduced file processing time by 97% and reached a … Read more

I created an automation that can import tens of thousands of documents into an n8n RAG (Retrieval-Augmented Generation) system with a single click. I recorded this process for The AI Automators to show how to build an orchestrator workflow, run parallel ingestion jobs, and automatically recover from failures without manual intervention. This article breaks down … Read more

I built a full evaluation system for RAG agents running in n8n and I want to show you how I do it. I recorded a video walking through everything, and this article captures that workflow in written form. I’ll explain why random “vibe testing” doesn’t scale, how to create a ground truth data set, how … Read more

Introduction I created an automation that takes nearly any file type — documents, slides, spreadsheets, images, audio — and turns it into consistent, searchable content for a Retrieval-Augmented Generation (RAG) agent. In the accompanying walkthrough I show three practical ways to do this: LlamaParse for ease and breadth, Docling for a self-hosted route, and Mistral … Read more

I created a multi-user AI agent that runs inside an e-commerce dashboard and can answer account-specific questions like “Where is my order?” or “Show me the invoice for order #6.” I built the agent with n8n and Supabase, and I focused heavily on security. In this article I’ll explain the full architecture, show the demo … Read more