Alan Walsh

Introduction I created an automation that takes nearly any file type — documents, slides, spreadsheets, images, audio — and turns it into consistent, searchable content for a Retrieval-Augmented Generation (RAG) agent. In the accompanying walkthrough I show three practical ways to do this: LlamaParse for ease and breadth, Docling for a self-hosted route, and Mistral … Read more

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

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for retrieving information from vector databases, but they often face a common challenge: the data retrieved can be outdated or irrelevant because it lacks precise filtering based on metadata.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

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

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

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