So I’m building my first real RAG system in Latenode, and I’m honestly overwhelmed by the model selection. Having 400+ models in one place is great until you realize you have to pick which ones actually work together.
My understanding is that RAG has two main parts: you need a retriever model to search through documents and find relevant chunks, and a generator model to compose answers based on what was retrieved.
But when you have 400+ options, how do you actually pick? Do I match based on price? Speed? The type of data I’m retrieving? Do some retrievers just inherently work better with certain generators?
I don’t want to overthink this, but I also don’t want to pick the wrong combination and end up with poor retrieval or weak answer generation. What’s your mental model for choosing?
The good news is you don’t need to test all 400. You pick based on your specific job.
For retrieval, you want a model that’s built for semantic search and ranking—something that can understand document relevance. For generation, you want something that’s good at writing clear, concise answers.
Here’s what I do: start with a proven pair. Claude for generation because it handles citations well. For retrieval, use one of the embedding models that Latenode offers that specializes in semantic similarity.
Once you have that baseline working, you can swap models and benchmark the results. Does a cheaper model hurt quality? Does a faster one lose accuracy?
Don’t try to optimize before you have something running. Get the workflow live, then iterate.
I went through this exact decision paralysis. My approach is: retriever and generator don’t need to be from the same family or provider. They’re independent pieces.
For retrieval, I look for models that score well on semantic search benchmarks. For generation, I match it to the tone and format I need. If I need technical answers, I pick a model that’s been trained on technical content. If I need conversational answers, I pick differently.
Cost does matter, but it’s not the only factor. I’ve found that a slightly more expensive retriever that ranks documents correctly saves me money long-term because the generator works with better input.
The practical approach I settled on was to start with popular pairings that others in the community have validated. OpenAI for retrieval, Claude for generation—or vice versa. These combinations have been tested across different document types, so you inherit knowledge from the community.
Once that baseline works for your use case, you can experiment with alternatives. Swap the retriever to see if accuracy improves. Try a cheaper generator to cut costs. Small changes help you understand which variables matter for your specific data.
Model selection for RAG typically follows two principles: retriever specialization and generator capability. Retrieval models should be optimized for ranking and semantic similarity, while generation models should prioritize output quality and format control.
Practically, start with models that have established RAG benchmarks. Test against your actual documents rather than hypothetical scenarios. Measure both retrieval precision and generation fidelity. Many teams find that a strong retriever with a moderate generator outperforms a weak retriever with a powerful generator.