I’ve been building RAG workflows in Latenode and keep running into this decision paralysis. When you’re assembling a retrieval-and-answer pipeline, you need to pick models for different stages—maybe Claude for synthesis, GPT for ranking, something else for the final answer generation. But with 400+ models available through one subscription, how do you actually decide?
I started just picking whatever was popular, but that doesn’t always work. A smaller, faster model might nail retrieval speed, while a heavier one gives better answers. The cost math changes too when you’re not managing separate API keys and billing per provider.
Does anyone have a practical approach? Do you benchmark them first, or just try a combo and iterate? I’m curious if the actual performance difference justifies swapping models around, or if I’m overthinking this.
The trick is that you don’t have to guess. In Latenode, you can wire up a workflow that tests different model combinations without rewriting anything. Set up your retrieval step, then duplicate it with different models, run them in parallel on test data, and see which one gives you better results.
I’ve found that the retrieval step doesn’t always need the biggest model. Something like Mistral can be fast and cheap there. But synthesis and answer generation? That’s where the bigger models earn their keep. Since you’re paying one subscription for all of them, you can actually afford to be intelligent about this instead of picking one model and hoping.
Build a small test workflow first with your actual data, try three or four model combinations, measure what matters to you (speed, accuracy, cost per query), then lock it in. The visual builder makes this quick.
I started thinking about this wrong. I was treating model selection like a one-time decision, but really it’s more about understanding what each stage actually needs.
For retrieval and ranking, you want speed and consistency. You don’t need reasoning power there. For synthesis and generating the answer, you want deeper models that can actually understand context and nuance.
What helped me was just running a test. I built a small RAG workflow with three different combinations and threw a batch of questions at it. One combo was fast but sometimes missed the point. Another was slow but accurate. The balanced one did both reasonably well.
You can iterate fast in Latenode because you’re not managing API keys and billing across five different services. Just change the model, run it again, see what happens.
The honest answer is you probably don’t need the best model at every step. Retrieval is mostly pattern matching, so a capable smaller model works fine. The ranking step benefits from some intelligence but doesn’t need to be genius-level. Generation is where you want power.
I’ve found that testing with real data beats theory every time. Set up your pipeline, run five common questions through it, see which models felt right. Took me maybe an hour to figure out what actually worked for my use case instead of guessing.