The AI Copilot Workflow Generation feature is interesting in theory. You describe what you want in plain language, and the system generates a ready-to-run workflow.
But I’m skeptical about how well this actually works in practice. When I’ve used code generators or AI builders before, the output is usually “close” but needs real customization.
For RAG workflows specifically, I’m wondering if describing “build me a workflow that retrieves customer data from our help docs and generates support responses” would actually result in something functional, or if it’s more of a starting point that still needs significant work.
Has anyone used the AI Copilot to generate a RAG workflow from a plain English description? Did it actually work, or was it mostly useful as a template that needed heavy editing?
I’ve used it multiple times and it’s genuinely useful. The key is being specific about what you want.
Instead of “build a RAG workflow,” say “create a workflow that retrieves documents about billing from our knowledge base when a customer asks about charges, then generate a response.”
The AI Copilot takes that description and generates a visual workflow with all the blocks connected correctly. Retrieval block, generation block, structured prompt—all there.
Does it get everything perfect? No. But it’s 80% of the way to working. You might adjust the prompt or add error handling, but the architecture is solid.
I use it for the baseline structure every time. Beats starting from scratch, and the visual workflow is something I can immediately understand and modify in the builder.
The AI Copilot saves me hours on scaffolding. Then I customize for my specific use case.
Used it for a document processing workflow. Described what I needed: pull contracts, extract key terms, flag compliance issues.
The generated workflow had all the right blocks in the right order. I didn’t have to build it from scratch. Just tweaked a few prompts and connected it to our actual document storage.
It wasn’t perfect, but it was functional within an hour. Starting from blank would have taken half a day.
The AI Copilot works best when your use case is common. If you’re doing something off-the-wall, the generated workflow might miss the mark. But for standard RAG use cases—support, research, data processing—it gets the fundamentals right.
The time savings is real. You get a working prototype in minutes instead of building from templates manually.
Generative workflow tools have improved significantly. The output quality depends on description precision. More specific instructions yield better results.