Automations

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

I created an AI agent that can query databases effectively using natural language. This agent can handle everything from simple single-table queries to complex database schemas with multiple relationships. In this article, I’ll walk you through the entire process, demonstrating how to build such an agent using a Postgres database hosted on Supabase. While I … Read more

GraphRAG is one of the most effective ways to improve the accuracy and reliability of AI agents. Many people hesitate to use knowledge graphs because they seem complicated to set up and hard to maintain. In this article, I’ll show you how to quickly set up your own knowledge graph, automatically populate it from your … Read more

RAG agents are incredibly powerful tools for retrieving information from vector databases, but they often face a common challenge: the data retrieved can be outdated or irrelevant because it lacks precise filtering based on metadata. To solve this, I created an automation that allows agents to generate advanced metadata filters dynamically. This ensures they pull … Read more

In this article, I’ll share how I built a complex full-stack web application using no-code tools over just three days. The app, Insights LM, is a fully functional clone of Google’s NotebookLM, packed with advanced features like file uploads, AI-powered document chat using Retrieval-Augmented Generation (RAG), inline citations, podcast generation, and more. I achieved this … Read more

In building AI-powered Retrieval-Augmented Generation (RAG) agents using n8n, I discovered a simple adjustment that can dramatically improve their effectiveness. This tweak takes just about ten seconds to implement but can make a significant difference in how well your agents understand and retrieve information. Even experienced users might overlook this detail, yet it’s one of … Read more

Context engineering has become a crucial skill for anyone working with AI agents today. As these agents grow more advanced and capable of independent operation—accessing the web, knowledge bases, and various tools—the traditional approach of prompt engineering no longer suffices. The key challenge is managing the context window, which is the limited amount of text … Read more