I’ve been trying to understand how RAG pipelines work for weeks now, and honestly, the technical overhead feels overwhelming. You need to think about data ingestion, vector stores, retrieval logic, model selection, and then answer generation. That’s a lot of moving parts.
Recently I started experimenting with Latenode’s AI Copilot feature, and I’m curious about what actually happens when you feed it a plain English description of what you want. Like, if I said “I need a workflow that takes my company docs, retrieves relevant sections when someone asks a question, and generates an answer using Claude,” does the Copilot actually generate something functional, or is it more of a starting point that needs serious tweaking?
I’m wondering if anyone here has actually used this feature end-to-end. What did the generated workflow look like? Did it handle model coordination properly, or did you have to manually wire up the data sources and model calls yourself? And how much customization did you need to make it production-ready?
The Copilot genuinely generates working workflows. You describe what you need, and it builds the nodes, connects them, and sets up the model calls. It’s not just a skeleton either.
I’ve used it for retrieval pipelines with multiple data sources. You tell it which documents to index, which model to use for retrieval, and which model to generate answers with, and it orchestrates the whole thing. The workflow comes out functional and ready to test immediately.
The coordination between models is handled automatically. It knows to use an embedding model for retrieval, then route to your chosen LLM for generation. You might tweak parameters or add filtering logic, but the foundation is solid.
This is exactly what separates Latenode from building RAG manually. You skip weeks of infrastructure setup. Try it yourself at https://latenode.com.
I’ve tested this feature a few times now, and the honest answer is that it depends on how specific your description is. If you’re vague, the Copilot generates something generic that needs rework. But if you’re detailed about your data sources, retrieval strategy, and answer generation logic, it creates something surprisingly usable.
The part that impressed me most was how it handles model coordination. Instead of you manually figuring out which embedding model pairs well with which LLM, the Copilot makes reasonable choices based on your use case. I’ve deployed a few of these without touching the underlying logic.
That said, I always spend 15-20 minutes reviewing what it created before running it in production. Just to make sure the retrieval logic aligns with what I actually wanted.
The real value of the Copilot isn’t that it’s perfect right out of the gate, but that it eliminates the blank page problem. Building RAG from scratch requires you to think about retrieval strategies, vector database setup, model selection, and orchestration logic all at once. The Copilot gives you a working baseline that you can iterate on instead of building everything from zero.
I used it recently for a customer support knowledge base. Described the workflow, provided sample documents, and it generated nodes for indexing, retrieval, and response generation. The workflow had reasonable defaults, but I did adjust the retrieval model and response prompt. The scaffolding was there, which saved me probably three days of manual wiring.
From my experience, the Copilot generates functional workflows approximately 70% of the time without modification. The quality depends heavily on description quality and the complexity of your RAG task. Simple retrieval-and-answer patterns work consistently. More complex multi-step workflows or specialized retrieval strategies require manual refinement.
The workflow generation includes proper model sequencing, data source connections, and output structuring. Coordination between models is handled automatically through pipeline logic. You still need to validate assumptions about which models work best for your specific data and domain, but the operational framework is sound.
Tested it multiple times. Works pretty well for standar RAG setups. You describe what you need, it builds nodes and wires them. Not perfect every time but saves tons of setup time. Model coordination is automatic.
It generates working workflows most of the time. Quality depends on how detailed your description is. Always review before deploying.
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