Automations

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for retrieving information from vector databases, but they often face a common challenge: the data retrieved can be outdated or irrelevant because it lacks precise filtering based on metadata.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

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

I created an automation that builds a multimodal Retrieval-Augmented Generation (RAG) agent capable of indexing and analyzing complex PDFs containing text, images, and tables at scale. This agent goes beyond simply extracting text—it understands images using an AI vision model and responds to queries with images and tables embedded directly in its answers. This approach … 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