Here’s what’s been confusing me: I have access to 400+ AI models through a single subscription, which is great, but it also means I’m paralyzed by choice when setting up RAG.
Obviously, retrieval and generation are different tasks. Some models might be better at understanding what information I’m looking for, and others might be better at generating coherent answers. But I don’t actually know which models excel at which task.
Does anyone have actual experience choosing? Like, does it matter if you use Claude for retrieval and GPT for generation, or is that overthinking it? Are there models that are specifically known for being better at one piece of the RAG puzzle?
I want to set this up intelligently instead of just picking randomly and hoping it works.
The good news is you don’t need to overthink this as much as you might think. Different models have different strengths, but the platform lets you experiment without any friction.
For retrieval, you generally want a model that’s strong at understanding context and relevance. For generation, you want one that’s good at producing coherent, accurate text. But here’s the thing—you can set up your workflow, test it, and swap models in seconds. There’s no cost penalty for trying different combinations.
What I’ve found works well is using a tighter, faster model for retrieval since that task is about finding relevance, and a stronger model for generation since that’s where quality matters most. But honestly, many models perform well at both.
With Latenode, you’re not locked into one choice. You build it, see how it performs, and iterate. The fact that all 400+ models are under one subscription means you can actually do this kind of experimentation without worrying about API costs exploding.
I ended up pairing a retrieval-focused model for the context extraction phase and a stronger general model for generation. The retrieval model handles understanding what information is relevant, and the generation model turns that into a proper response.
But I also tested the other way around and got decent results. The difference wasn’t massive. What mattered more was making sure the retrieval actually pulled the right documents and that the generation had enough context to work with.
Start with a reasonable pairing, test it with actual queries, and adjust from there. You’ll learn what works for your specific use case faster than trying to predict it upfront.
Model selection depends on your specific task requirements and data characteristics. Faster models work well for retrieval because speed matters when you’re searching through content. Generation benefits from more capable models since output quality directly impacts user experience. However, the workflow testing itself is where real insights emerge. Most teams find that after initial configuration, minor model adjustments yield diminishing returns compared to improving retrieval accuracy and context quality.