Stop Building n8n AI Agents. Build LLM Chains Instead
💼 Business owner or operator with a team? We build AI automation systems that cut costs and scale ops — done for you: https://ryanandmattdatascience.com/ai... 🚀 Want to make money with AI skills? Join our free community — real projects, real client strategies, and the exact stack we use: https://www.skool.com/data-and-ai 🍿 WATCH NEXT n8n Course: • n8n Full Course (Free Bootcamp) - Learn Ho... In this video, I break down the basic LLM chain node in N8N and explain exactly when you should use it instead of an AI agent. The key difference is simple: basic LLM chains give you access to large language models like GPT or Claude without the extras like memory or tools that AI agents provide. I walk through six practical examples showing how to use LLM chains effectively, including working with chat triggers, system prompts versus user prompts, structured output parsers for consistent data formatting, fallback models for reliability, and sequential LLM chains for multi-step workflows. You'll see real examples of each setup, including some common mistakes to avoid like trying to use AI/user prompt combinations that can cause hallucinations. By the end of this tutorial, you'll understand exactly when a basic LLM chain is the right choice for your automation versus when you need the full power of an AI agent. I cover everything from simple prompt-and-response setups to more advanced structured outputs that ensure your data flows cleanly between nodes. Whether you're researching information, formatting outputs, or chaining multiple LLM calls together, this video gives you the practical knowledge to build better N8N workflows with AI. If you want to join other people learning AI automation, check out my free school community linked in the description below. TIMESTAMPS 00:00 Introduction to Basic LLM Chain 01:00 LLM Chain vs AI Agent - Key Differences 02:08 Finding and Setting Up Basic LLM Chain 03:01 Choosing Your Model and API Setup 03:50 Chat Trigger vs Manual Trigger 05:01 Understanding AI, System, and User Prompts 06:17 Practical Example: Music Artist Research 08:15 Output Parser for Structured Formatting 10:10 Fallback Models for Critical Operations 10:45 Sequential LLM Chains - Multiple Tasks 13:00 Best Practices and Final Tips OTHER SOCIALS: Ryan’s LinkedIn: / ryan-p-nolan Matt’s LinkedIn: / matt-payne-ceo Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.
