Agentic RAG

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

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 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 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

More companies are moving towards running their AI agents and systems entirely on local hardware. This shift is motivated by concerns over privacy, compliance, reducing cloud dependency, and cutting operational costs. The primary business use case for AI right now is enabling employees to interact with AI agents that are deeply informed by the company’s … Read more

I created an automation that allows AI agents to answer questions based on spreadsheet and database data accurately and efficiently. Traditional vector stores often struggle with this task, especially when it comes to structured data like spreadsheets. They usually return poor results, as semantic search retrieves chunks of documents that are often out of context. … Read more