I’ve been building more RAG workflows lately, and I keep running into this interesting problem. With Latenode’s access to 400+ AI models, I should feel like I have more options, but honestly it sometimes feels like the opposite.
Like, for the retrieval step, should I use a smaller, faster embedding model? For reranking, does it matter if I pick Claude versus OpenAI? And for the final generation, do I care about cost or speed more?
In theory, having options is great. But I find myself overthinking it. Do I really need to test five different model combinations when I could probably just pick a solid default for each stage and get moving?
I’m curious if people have developed any heuristics for this. Are there certain models that are just known to work better for specific RAG roles? Or do you really need to run experiments to figure out what works for your particular use case?
The beauty of Latenode is that you don’t need to overthink it. Start simple. Use Claude for generation, a standard embedding model for retrieval, and stay in the same ecosystem.
Then—and this matters—monitor your results. If your answers are good, you’re done. If response time is slow, swap to a faster model. If cost is high, try a smaller one.
The 400+ models aren’t a problem to solve, they’re a toolkit. You use what you need. One subscription covers all of them, so switching models is free. That means you can iterate without API key chaos or billing nightmares.
Start boring. Get data. Make decisions based on real performance, not theoretical best-ness.
I actually keep things really straightforward. I use Claude for text generation, stick with a standard embedding model for retrieval, and don’t second-guess myself. The results are solid, and I’m not paying for multiple API subscriptions.
Where I do vary models is based on specific constraints. If speed matters more than quality, I’ll drop down to a smaller model for reranking. If cost is a concern, I test a cheaper alternative. But these are decisions I make after the system is running, not before.
The paralysis part is real though. I think it helps to remember that with Latenode, switching models is almost free. There’s no lock-in. So picking a reasonable starting point and adjusting based on actual performance is totally fine.
Model selection should follow your constraints: speed, cost, or accuracy. For retrieval, consistency matters more than novelty. For generation, Claude and OpenAI are reliable defaults. Start there, monitor performance metrics, then optimize based on your specific requirements rather than exploring all options upfront.