I’ve been testing different AI models like ChatGPT and others to build n8n automation workflows. The results have been pretty frustrating so far.
Most of these AI tools can explain the general approach and break down the steps, but they really struggle with the technical implementation:
- Creating proper JSON structure that n8n actually accepts
- Getting the node connections and data flow right
- Keeping track of variables and context in complex automations
I’m working on an automated inventory system that needs to update product info, monitor stock levels, and connect with multiple platforms. Every time I try to get an AI to generate the workflow, I end up having to fix so much stuff that it’s almost easier to start from scratch.
Wanted to ask:
- Has anyone here had better luck with AI-generated n8n workflows?
- Are there any specific prompts or methods that actually produce working results?
- Maybe there’s a tool or approach I’m missing that makes this more reliable?
I had some success after completely switching my approach. Instead of asking AI to generate full workflows, I break everything down into individual nodes and test each piece separately. What worked: create a simple template structure in n8n first, then use ChatGPT for specific node parameters and expressions. I’ll ask it to write JavaScript code for Function nodes or help construct API calls for HTTP Request nodes. The breakthrough was realizing AI handles isolated technical problems way better than understanding n8n’s overall workflow architecture. I keep a document with working examples of common patterns like error handling and data mapping for reference when prompting. For your inventory system, start with just the product update functionality. Get that working manually, then use AI to optimize individual components. You’re right about the context switching issue - these models can’t maintain the complex state relationships that n8n workflows need.
i’ve totally given up on AI making full n8n workflows. instead, i found it better for debugging - just share your broken workflow JSON n ask it to find the errors. claude’s def better than chatgpt for this. also, try asking for 1 piece at a time - like just the webhook or just data transform, not the whole thing.
The game-changer for me was treating AI like a documentation helper, not a workflow builder. I started feeding ChatGPT the actual n8n node docs along with what I needed, and the results got way better. I made a personal prompt template that includes the exact node schemas and data structures straight from n8n’s official docs. Basically, I copy relevant chunks from the n8n documentation and paste them into my prompts before asking for specific implementations. This fixed most of those JSON structure headaches you’re dealing with. For complex stuff like your inventory system, grab one of n8n’s workflow templates first, then use AI to tweak specific parts while keeping the official docs handy. The context issues mostly vanish when you give the AI real examples of how data moves between nodes in working workflows.