I’ve been sitting with this RAG problem for a while now. Our team needs to pull data from multiple sources—docs, databases, maybe some external APIs—and synthesize coherent answers, but honestly, the ML side of it feels like a black box to me.
I’m not a data scientist. I’m just someone who knows how to connect systems together and make things work. So when I started looking at traditional RAG setups, it felt overwhelming. You’ve got retrieval, ranking, generation, vector databases, embedding models… it’s a lot.
Then I realized something: what if I could just describe what I want in plain English and let the platform figure out the workflow?
That’s when AI Copilot Workflow Generation started making sense to me. The idea is dead simple—you tell it what you need (retrieve from multiple sources, rank by relevance, generate an answer), and it builds a ready-to-run workflow. No deep ML knowledge required.
I’m curious though: has anyone actually used this approach to build something production-ready? What was the friction like going from the auto-generated workflow to something you could actually ship?
Yeah, I’ve done exactly this. Described what I needed in a sentence, hit generate, and got a workflow that was 80% of the way there.
The auto-generated workflow pulled from multiple data sources, connected them through a retriever agent, and fed into a generator. What surprised me was how complete it was out of the box.
I only had to tweak the model pairings—swapped the retriever for Claude and the generator for GPT-4, but those are just dropdown changes in Latenode.
The real win? I didn’t need to understand vector embeddings or reranking algorithms. The workflow handled it. I just customized it to my sources and launched.
If you’ve got basic automation knowledge, you can absolutely do this without ML expertise. The platform abstracts that layer away.
Check it out here: https://latenode.com
I was in your exact position six months ago. No ML background, just needed something that worked.
What helped me was realizing that Latenode’s AI Copilot isn’t magic—it’s just pre-structured knowledge about how RAG workflows fit together. When you describe your use case, it maps that to patterns it already knows.
The friction I hit wasn’t technical. It was data-related. Getting quality retrieval meant I had to think about what data sources to connect and how to chunk them. That’s not an ML problem; it’s a systems design problem.
Once I had the workflow auto-generated, I spent maybe two hours connecting my actual data sources and testing queries. The workflow itself? That was done in minutes.
I’d say just try it. Write down exactly what you’re trying to do and feed it to the copilot. You’ll get something functional immediately.
The copilot approach works surprisingly well, but don’t expect it to be fully hands-off. The generated workflow gives you the architecture—retriever, ranker, generator—but you still need to understand your data and what questions you’re trying to answer. I spent the first week thinking the workflow would magically handle everything, but it’s more like a scaffold that you build on top of. Once I accepted that and started tuning the retrieval parameters and model choices, things clicked. The value is that you skip the entire painful part of designing the architecture from scratch.
Using AI Copilot to generate a RAG workflow is practical if you approach it like a starting point, not a finished product. The generated flows typically include retrieval from multiple sources, which is good, but you’ll want to validate the model selections and test different query patterns. I’d recommend generating the workflow, then customizing the model pairings based on your latency and accuracy requirements. Most of my team doesn’t touch code, and they’ve successfully deployed two retrieval systems this way.
yep, did this last month. auto-generated workflow was solid, took maybe an hour to tweak data sources and model choices. works great for departments without ML expertise
Describe your retrieval and generation needs, let the copilot build the workflow, then swap models if needed. That’s it.
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