How do you actually start building RAG when you've never done it before?

Other Questions RAG
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I’ve been hearing a lot about RAG lately and honestly it seems like everyone expects you to already know what you’re doing. The concept makes sense to me—retrieve relevant documents, then use an LLM to generate answers based on what you found. But when I started looking at how to actually build it, I realized there are so many moving parts.

I tried piecing together a basic setup myself and ran into the retriever needing to talk to the ranker, which then needs to hand off to the generator. That coordination felt messy. Plus I had no idea which models to use for each step or how to know if my choices were even good.

Then I discovered that Latenode’s AI Copilot can take a plain description of what I want—like “build me a RAG system that answers questions about our product docs”—and it actually generates a working workflow. No having to figure out the architecture myself.

What I’m curious about: for people who’ve built RAG before, does the Copilot approach actually save you real time, or does it just move the work around?

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The Copilot saves you massive time because it handles the architecture thinking for you. Instead of researching which retriever works with which ranker, you describe what you need and it builds the workflow.

I used it last month to stand up a customer support RAG system. Took me maybe 20 minutes from idea to having something I could test. Manually, that would’ve been hours of configuration.

The real win is that you can iterate fast. Change your description, regenerate. Test different approaches without rebuilding from scratch.

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