Daniel Walsh

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

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

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

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

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

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

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

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