So I’ve been exploring Latenode more, and having access to 400+ models in one subscription is honestly overwhelming when you’re designing a RAG workflow.
The thing is, RAG workflows usually have two distinct phases: retrieval (finding relevant documents) and generation (answering based on those documents). Different models might be better suited for each step. But with that many options, I’m stuck deciding.
Do you pick the same model for both steps? Does it matter if you use Claude for retrieval and GPT for generation? Is there a performance difference? Cost difference? Does it even matter as long as the workflow produces decent answers?
I’m especially curious about how people approach this when they’re building something in production and cost actually matters. Do you experiment with different combinations, or is there a general pattern that works most of the time?
This is exactly why having 400+ models in one place actually becomes an advantage. You’re not locked into any single model provider.
In practice, I’ve found that retrieval is less demanding than generation. A smaller, faster model often does fine for retrieval—you’re essentially matching documents to a query. But generation—the actual answer synthesis—benefits from a larger, more capable model.
The cost difference is real too. If you use an expensive model for retrieval when a cheaper one works just as well, you’re burning budget unnecessarily. So my pattern is: lean model for retrieval, better model for generation.
Latenode’s visual builder lets you test this quickly. You can swap models without rewriting code, see the output, and measure the impact. That’s where the unified subscription really pays off—experimentation gets cheap.
I struggled with this exact decision. What helped was thinking about what each step actually does. Retrieval is pattern matching—are these docs relevant to the query? That’s a lower-complexity task. Generation is synthesis—turning those docs into a coherent answer. That needs more reasoning power.
I started using a smaller model for retrieval and a larger one for generation. The workflow runs faster, costs less, and the output is actually better because each model is doing what it’s good at.
The tradeoff I found was that if retrieval isn’t precise enough, generation can’t salvage bad source material. So I spent more time tuning the retrieval step than I expected.
The paralysis is real, but here’s what I learned: most modern LLMs are actually capable enough that the choice doesn’t break your system. The bigger variable is your retrieval strategy—how many documents you fetch, how you rank them, what context you pass to the generator.
Start with a reasonable model pair (something like Claude for retrieval, GPT-4 for generation, or vice versa) and iterate on the actual workflow logic first. Once that’s solid, then experiment with swapping models if cost is a concern.
Having evaluated this across multiple deployments, the optimal strategy depends on your latency and cost constraints. For real-time applications, use faster models for retrieval passage ranking. For synthesis, prioritize accuracy. The platform’s ability to quickly test different combinations removes the guesswork.