I was skeptical about this at first. The whole idea of describing what you want in plain English and having an AI generate a working RAG workflow sounded like marketing hype.
But I actually tried it with Latenode’s AI Copilot feature last week. I wrote something like “create a workflow that retrieves customer FAQs and uses AI to generate answers to support tickets.” And it actually generated most of the workflow for me. Not perfect, but legitimately usable.
What’s interesting is that it didn’t just create a skeleton—it actually wired together retrieval logic, connected it to a generation model, and set up data flow between the steps. I had to tweak a few things, sure. Like specifying which knowledge base to use and picking the right models from the available options.
The thing that surprised me is that the Copilot understood context. It wasn’t just pattern matching. When I described what I was trying to do, it inferred the logical structure needed and built something that made sense.
I’ve built a few workflows by hand since then to compare. Hand-built ones took longer but I had more control. Copilot-generated ones were faster but needed more iteration. For straightforward RAG patterns though, the Copilot got me to working code in minutes.
Has anyone else tested this? Does the Copilot stay useful when you’re building more complex workflows, or does it break down when you get beyond basic retrieval-generation patterns?
It’s definitely not just a demo. I’ve been using AI Copilot for probably six months now and it’s legitimately productive.
The way it works is you describe what you want, it generates the workflow, and then you customize it. For RAG specifically, it understands the basic pattern—retriever, generator, sometimes a preprocessor step. It’ll create those nodes for you.
Where it shines is when you’re building variations on existing patterns. Tell it you want a similar workflow but for a different data source, and it adapts fast.
I used it recently to build a workflow that coordinates multiple retrieval sources and sends results to three different generator models in parallel. Described it in plain English, got something pretty close, then fine-tuned from there.
The time savings are real. Before, I’d either hand-code or template stuff. Now I describe and iterate. Faster to working version.
The Copilot works well for standard patterns but struggles with unusual workflows. For basic RAG—retrieve, generate, output—it nails it. When you need something custom, it helps you get 70% of the way there.
I think the real value isn’t that it’s perfect every time. It’s that you can iterate faster. You get a working draft immediately instead of building from scratch. Then you can experiment with different retriever or generator models, add validation steps, whatever your use case needs.
The constraint is that you still need to understand what you’re building. You can’t just ask for something vague and expect magic. But if you know your requirements and describe them clearly, the Copilot handles the mechanical work.
The Copilot addresses a real problem: workflow scaffolding takes time even when you know exactly what you want. Translating that knowledge into a visual workflow requires clicking through nodes, connecting pipes, configuring each step. The Copilot automates that mechanical work.
For RAG workflows specifically, it’s effective because RAG follows a predictable structure. Retrieve, process, generate. The Copilot understands that pattern and can build it without user guidance. When you need something outside that pattern, it becomes less useful but still provides a foundation to build from.
Plain text to workflow generation works for familiar patterns. RAG is familiar enough that AI Copilot handles it well. What matters is whether your specific requirements match what the Copilot expects. For standard retrieval-generation workflows, expect high success. For specialized cases, treat the output as a draft.