I've been more active in the AIS+ community lately, but I think this workflow could be useful here as well. Most n8n chatbot tutorials use RAG: vector store, embeddings, retrieval chain, and so on. It works, but you've got lots of nodes, slower responses, higher API costs, and sometimes totally wrong answers. I wanted to simplify this. The result is a fully functional 4-node chatbot: Chat Trigger → AI Agent → OpenRouter LLM + Redis memory. The entire knowledge base lives in the system prompt. So, there is no need for a vector store or embeddings. Just clean, direct context. For a small business use case (knowledge base with 5,000–10,000 words of content) it handles customer questions accurately, costs roughly $1 per 1,000 messages, and the whole thing takes about 10 minutes to build from scratch. For the advanced folks: what do you dislike about this approach? I know the obvious ceiling is context window size - that's why the knowledge base must stay under 20 pages/14,000 tokens. But are there other failure modes I'm not thinking about? Would love a real critique. For the beginners: If you've been wanting to build your first chatbot but the RAG tutorials felt overwhelming, this might be a good starting point. I put together a step-by-step video walkthrough covering everything from scratch: setting up OpenRouter and Redis, configuring credentials, building the workflow, etc. No coding required, and the workflow is included, though my recommendation is to build this simple chatbot yourself. https://www.youtube.com/watch?v=ZLTZjXvXMrc

In this video I'm going to be telling you why you need to stop selling AI Agents and sell AI Solutions instead. AI agents are not what they were 6 months ago, because now everyone has hopped on the trend of selling AI agents. So to make a lot of money selling AI in 2025, you need to be different, and to stand out by selling AI solutions. It's a game changer! So grab your pen and paper, and get ready to take some notes. Hope you enjoy!

In this video I walk through a live step-by-step build of an AI-powered newsletter using no code. All you have to do is set your search topic and the system takes care of the rest. It starts by doing initial research, then an agent plans out the structure of the newsletter. From there, each section gets its own research and is written by different agents. An editor agent pulls everything together, adds the sources so you know exactly where the information came from, and creates a clean final draft. At the end, the system even sends the draft straight to your inbox so you can forward or share it however you like. I recorded this one live so you can see why I’m connecting nodes, how variables work, and how the whole workflow comes together in about half an hour. RESOURCE PACK HERE

In this video, I showcase the Ultimate Media AI Agent that combines powerful personal assistant features with advanced creative tools — and I’m giving it all away for free. This agent can manage emails, calendars, contacts, and Google Drive, while also creating and editing images, making and editing videos, turning images into videos, posting content across social media, scraping social platforms for research, and compiling results into Google Docs. It even has full web search capabilities. One of its best features is complete activity logging, so you can see every action it takes, whether successful or not, along with full input, output, and token usage details. Stick around until the end of the video, where I’ll share all the resources, templates, and workflows you need to set it up yourself. Google Doc Cost Breakdown & Setup Instructions Google Sheet Template