I’ve been wrestling with RAG pipelines for a while now, and honestly, the traditional approach is kind of a nightmare. You’re managing vector stores, dealing with chunking strategies, figuring out embedding models—it’s a lot of moving parts. But I started looking at how the AI Copilot in Latenode approaches this problem differently.
The idea is pretty straightforward: you describe what you need in plain English—something like “I need a workflow that pulls data from our knowledge base and answers customer questions”—and the copilot generates a ready-to-run RAG workflow. Not a template. Not a scaffold. An actual workflow.
What strikes me is that this actually handles the two hardest parts of RAG: data retrieval and answer synthesis. The copilot apparently understands both pieces and assembles them into something you can immediately test. You’re not left staring at a blank canvas wondering where to start or realizing mid-way that your retrieval strategy doesn’t match your generation strategy.
From what I’ve gathered, the copilot uses your description to infer the right nodes—document processing for retrieval, AI integration for generation, data flow between them. It’s not magic, but it fundamentally changes the workflow because you skip the “figure out the architecture” phase that usually takes days.
Has anyone here actually used the copilot to build a RAG workflow from scratch? Did it actually produce something you could run immediately, or did you need significant tweaking?