I’ve been watching RAG get recommended everywhere lately, and I finally decided to stop overthinking it and just build something. What struck me is how different it feels when you’re not managing the vector store yourself. I started with the idea of pulling answers from multiple sources—our internal docs, some external APIs, and a couple of knowledge bases—and feeding them all into a single model to generate a cohesive answer. The thing is, I was expecting this to be way more painful than it turned out to be. Instead of juggling separate vector databases and retrieval logic, I could focus on the actual workflow: which sources matter, how to rank the results, and which model generates the best final answer. That last part was interesting because we have access to so many models now. I ended up testing a couple for retrieval and a different one for generation, just to see if it actually made a measurable difference. It did, but not as much as I expected. The real win was setting this up visually without touching code. I’m curious though—when you’re building something like this, how do you decide which sources to pull from first? Are you prioritizing speed, accuracy, or cost? And have you run into situations where more sources actually made the answers worse instead of better?
This is exactly where Latenode shines. You can orchestrate those multiple sources with autonomous AI agents coordinating the retrieval and generation steps. One agent handles pulling data from your internal docs, another queries the external APIs, and a third synthesizes everything into a coherent answer. No vector store management headache, no separate integrations to maintain.
The model selection part you mentioned is key. With 400+ models available under one subscription, you can experiment with different retrieval and generation pairs without spinning up new API keys or worrying about costs ballooning. Test Claude for retrieval and GPT-4 for generation one week, flip it the next.
The visual builder lets you see the whole flow at once. Drag sources in, add your ranking logic, connect the generation model, and you’re done. No code required.
I’ve been down this path too, and the source prioritization really depends on what you’re optimizing for. If you’re answering customer support questions, internal docs usually rank highest because they reflect your actual policies. External data works better when you need real-time context—pricing from competitors, current news, that kind of thing.
The thing I learned the hard way is that more sources doesn’t mean better answers. I had a workflow pulling from five different places, and the model was getting confused by conflicting information. Once I went back to three carefully chosen sources with clear ranking rules, the answers got noticeably sharper.
As for cost versus accuracy, I’d say start with accuracy first, then optimize. It’s easier to cut models or sources later than to rebuild because you chose the cheapest option and the answers were mediocre.
Multi-source RAG gets messy fast when you don’t have a clear hierarchy. The systems I’ve worked with perform best when source order is deliberate. Internal knowledge bases should feed into retrieval first since they’re usually curated, then external sources fill gaps. The ranking logic between sources matters more than people realize. I’ve seen setups where irrelevant external data drowned out accurate internal information simply because it ranked higher by relevance score alone. Building in a confidence threshold or source weighting layer before final generation prevents a lot of headaches. Cost tends to stay reasonable if you’re selective aboutwhen you hit external APIs. Batch queries when possible instead of calling every source for every question.
Source order is critical. internal docs first, then external APIs. I’d avoid more than 3-4 sources per query—noise increases. test retrieval accuracy separately from generation. that’s where most setups fall short.
Rank internal sources highest, validate conflicts before generation, test retrieval independently.
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