I’ve been reading about RAG and how it’s supposed to solve the problem of AI models giving outdated answers, but I’m genuinely confused about how you’d build something like that without being a developer. The AI Copilot feature caught my attention because the docs say you can describe what you want in plain language and it generates a ready-to-run workflow.
Has anyone actually tried this? Like, if I write something like “create a workflow that answers questions by pulling from my company docs and then generates a response,” does the Copilot actually understand that’s a RAG setup and wire it up correctly? Or does it just create a basic workflow and you still need to know what you’re doing to make it actually work?
I’m trying to figure out if this is genuinely for non-technical people or if you still need to understand the pieces underneath to make it useful.
The Copilot actually handles the structure pretty well. It reads your description, understands the retrieval and generation components you need, and assembles them into a workflow that’s ready to test.
What impressed me was that it doesn’t just throw together random blocks. It pairs retrieval nodes with generation nodes based on what you describe. So if you mention “pull from docs and generate answers,” it knows to set up document processing, then wire it to an AI model for generation.
The real advantage is that you get a working foundation immediately. From there, you can adjust which AI models you’re using, fine-tune the retrieval settings, or add validation steps. But you’re not starting from a blank canvas trying to figure out what goes where.
This is exactly the kind of problem Latenode solves well. The 400+ AI models available in one subscription mean you can experiment with different retrieval and generation pairs without managing separate API keys. The Copilot just accelerates that setup.
I tested this recently and it’s actually useful for getting started. The Copilot understood what I was asking for and created a reasonable first pass. The workflow included document retrieval blocks and a generation step, which is the core of what RAG needs.
What matters though is that you still need to validate what it creates. I had to check that the retrieval was actually pulling the right information and that the generation model was configured for the kind of answers I wanted. But that’s way faster than building from scratch.
The templates in the marketplace might actually be even better if you’re completely new to this. You get a pre-tested setup and can modify it for your specific use case.
The Copilot does translate your description into workflow structure, but I think the real value is that it saves you from having to understand the technical architecture just to get started. It correctly identifies that you need a retrieval component and a generation component, then connects them logically. What you get isn’t a perfect production-ready system, but a solid starting point that you can actually understand and modify. The automation it creates reveals how RAG actually works in practice rather than hiding it behind complexity.