I’ve been thinking about this problem a lot: Latenode gives you access to 400+ AI models across different providers. That’s amazing in theory, but when you’re building a RAG system, you need to choose which model to use for retrieval and which for generation. Having that many options feels paralyzing rather than liberating.
Like, how do you actually approach this? Do you just pick popular models and iterate? Is there a performance framework you follow? Or is it more experimental—try one, see if it works, swap it out if it doesn’t?
I’m particularly curious about the difference between retrieval and generation. Do they need different kinds of models? I’ve heard that some models are better at understanding what you’re looking for (retrieval) while others excel at crafting coherent responses (generation). But is that distinction as important as it sounds, or can you kind of use any decent model for either step?
And practically speaking, when you’re billing per token or per API call, does the choice actually matter? Like, are you paying significantly more for Claude versus a smaller open-source model, or is it more about speed and quality?
Has anyone here actually gone through the process of selecting models for a RAG system? What made you choose what you chose, and did you switch models later once you had real usage data?
Having all those models available means you can pick the best one without worrying about managing separate API keys or integrations. That’s the real win.
For retrieval, you want a model that understands semantic similarity well. For generation, you want something that produces coherent, contextually relevant answers. They’re different tasks, so different models make sense.
Start with proven models. Claude for generation if you want quality and nuance. For retrieval, something optimized for embeddings. Then adjust based on your latency and cost requirements.
The fact that it’s all one subscription means you can actually experiment without bloating your infrastructure.
I started with popular models because I knew they’d work. Claude for generation, a solid retrieval-focused model for the semantic matching part. The difference between retrieval and generation is real—they need different strengths.
Once the basic RAG was running, I tested cheaper alternatives for generation and found a smaller model that worked almost as well. That reduced costs without hurting quality. Having access to so many options meant I could test without commitment.
The experimental part is key. You need real usage data to make smart choices.
Model selection depends on your retrieval and generation requirements. Retrieval models should excel at semantic understanding—matching query intent to relevant documents. Generation models should be coherent and contextually aware. These aren’t always the same model. Start with established options, measure performance on your actual queries, then swap based on results. Cost matters, but quality degradation from cheaper models often costs more in system reliability.
Retrieval and generation demand different model capabilities. Retrieval prioritizes semantic matching precision; generation prioritizes coherence and context awareness. Evaluate models on your actual data and queries. Cost per token varies, but quality consistency matters more for RAG reliability. Start conservative with established models, then optimize after gathering performance metrics.
Different models for retrieval vs generation make sense—they need different strengths. Start with proven options, test with real data, then optimize for cost.