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

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