Setting up a basic AI agent in n8n that queries a vector store is straightforward. However, ensuring that the agent consistently delivers relevant, high-quality answers takes some advanced techniques. One of the easiest and most effective ways to improve the accuracy of your retrieval-augmented generation (RAG) agent is by using re rankers. With the release … Read more

Fine-tuning is a powerful way to transform your AI agents, making them more specialized, reliable, and aligned with your unique tone of voice. In this article, I’ll walk you through how to get started with fine-tuning, explain why and when to use it, and share a system I built using Airtable and n8n that allows … Read more

NotebookLM stands out as one of the most powerful AI research tools available today. What makes it remarkable is its ability to ground responses exclusively in the sources you provide, ensuring accuracy and relevance. However, its closed nature limits customization and self-hosting, which can be a barrier for businesses wanting a tailored AI research assistant. … Read more

Building AI automations and agents involves understanding many concepts that help you create smarter and more efficient workflows. In this article, I will explain over one hundred of these essential concepts in a clear and practical way. This guide is meant to help you gain a solid grasp of AI automation, whether you’re just starting … Read more

I created an automation that explains how data flows and looping works within n8n. When building workflows, you often need to handle multiple items at once, whether those are rows from a spreadsheet or multiple results from an API call. Understanding how n8n manages these items and how to control the processing can make your … Read more

I created a comprehensive crash course to help you build automations and AI agents using n8n, guiding you from an absolute beginner to a confident creator of full workflows. By the end, you’ll understand key concepts that allow you to build more advanced automations. This article breaks down the process step-by-step, explaining technical ideas in … Read more

When building AI agents that rely on vector stores to fetch information from your own data, you might notice that the answers aren’t always accurate. The challenge often lies in how vector search handles queries. While it excels at understanding the meaning behind natural language queries, it can struggle with specific names, acronyms, codes, or … Read more

In my latest exploration of Make.com, I focused on its exciting new feature, the Model Context Protocol (MCP). This enables users to connect external agents, such as Claude Desktop, directly to their Make.com accounts. The beauty of this feature lies in its ability to let these agents discover, understand, and execute scenarios without requiring complex … Read more

I created an automation that simplifies the process of building Retrieval-Augmented Generation (RAG) systems using n8n and Supabase. This guide will walk you through each step, from understanding the basics to implementing advanced techniques, ensuring you can create a fully functional RAG system. Understanding RAG RAG combines traditional search methods with modern AI capabilities to … Read more

Creating client-branded applications doesn’t have to be complicated. In this guide, I’ll walk you through the process of building a multi-step article generation app using Lovable for the front end, Supabase for the backend, and n8n for automation—all without writing a line of code. Building client-branded applications involves several key concepts that can simplify the … Read more