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
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
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
OpenAI has recently introduced a groundbreaking update: the new GPT image model, now accessible via API. I created an automation that fully streamlines the process of creating infographic-style images for social media and blogs using n8n. This article will take you through the steps to set up this automation, allowing you to save time while … Read more
In this blog, I explore the ‘Lost Context Problem’ that often plagues RAG systems and present two innovative techniques: Late Chunking and Contextual Retrieval. These methods can significantly enhance the accuracy of your retrieval systems and minimize frustrating hallucinations. The Lost Context Problem The lost context problem is a significant challenge in retrieval-augmented generation (RAG) … Read more
In this blog, I dive into the recently launched AI agent system from Make.com and compare it against the established AI agents from n8n. By breaking down their features, usability, and overall performance, I aim to help you choose the best platform for your automation needs. User Experience (UX) Creating AI agents in n8n is … Read more
In this blog, I explore the revolutionary Agentic RAG blogging system I developed using N8N. This approach overcomes traditional limitations, enabling AI agents to seamlessly gather and synthesize information from multiple data sources, resulting in more accurate and engaging content. Demo of Agentic RAG In this section, I’ll walk you through a live demo of … Read more
In this blog, I explore Cache-Augmented Generation (CAG), a groundbreaking retrieval method that taps into server-side memory from OpenAI, Anthropic, and Google Gemini. I compare it with traditional Retrieval-Augmented Generation (RAG) to determine which method is more efficient and effective for various applications. Introduction to CAG and RAG Cache-Augmented Generation (CAG) and Retrieval-Augmented Generation (RAG) … Read more
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