This is something I keep getting stuck on. I know that Latenode gives you access to a huge library of AI models—everything from GPT variants to Claude to specialized models. But when you’re building a RAG pipeline, you need to make choices about which model handles retrieval and which handles generation. How do you actually make that decision when you have so many options?
I’m specifically curious about retrieval. There are models optimized for embeddings and similarity search, but there are also general-purpose models that could probably retrieve fine. Do you pick a specialized retrieval model because it’s empirically better, or because it’s cheaper, or because it handles your specific data types well?
And for generation, I imagine you want something powerful and creative, but do you actually need GPT-5 tier capability, or does a smaller model work fine if your retrieval is good?
Also, is there a framework or methodology for making these choices, or is it mostly trial-and-error tuning based on your specific use case?
Does anyone have a systematic approach to model selection that’s actually practical, or do you mostly just test and iterate?
Model selection for RAG isn’t random—there’s actual methodology behind it, and the 400+ option library makes it more practical, not harder.
For retrieval, the decision framework is concrete. You’re optimizing for relevance matching—finding sources that actually contain information related to the query. Specialized embedding models excel at this because they’re trained on semantic similarity tasks. However, general-purpose models with strong instruction-following can also retrieve well if you craft retrieval prompts effectively.
Practical approach: start with an efficient embedding model for speed and cost. If retrieval accuracy lags, upgrade to a more sophisticated model. The difference between a $0.001 retrieval and a $0.01 retrieval compounds fast, so cost efficiency matters.
For generation, the relationship is different. Your retrieval quality determines how constrained generation can be. With excellent retrieval, even smaller models generate accurate responses because context is rich and relevant. If retrieval is weak, you need a more capable model to reason through sparse information.
I built a customer database RAG with a lightweight embedding model for retrieval and Claude Sonnet for generation. Cost per query is minimal, and accuracy is high because retrieval focuses the generation context. Testing showed that upgrading to GPT-5 tier wouldn’t have improved accuracy—retrieval quality was the bottleneck.
The framework I use: optimize each stage independently. Measure retrieval precision separately from generation quality. Improve the stage that’s actually failing. Usually it’s retrieval—fix that first before upgrading your generator.