This has been my biggest blocker. I have access to GPT-4, Claude, Gemini, and a bunch of specialized models I barely know how to use. The question that keeps me up is: does the retrieval step actually care which model you use, or does it just matter for the generation step?
I’ve read that different models have different strengths. Some are better at understanding semantic meaning for retrieval, others are better at crafting coherent responses. But when you have 400+ models available in one platform, how do you even start deciding?
I’m also wondering if domain specialization matters here. Like, if I’m building something for legal documents, does it actually make sense to use a legal-specialized model, or am I overthinking this? And more importantly, does the platform give you any guidance on which models work well together for a RAG pipeline, or are you just experimenting until something sticks?
For retrieval, you want a model that’s strong at semantic understanding and can rank relevance accurately. Claude and Gemini tend to be solid choices here. For generation, you want a model that’s fluent and can handle context—GPT-4 and Claude again are reliable.
With domain-specific work like legal documents, absolutely use a domain-tailored model if you have one available. Latenode gives you access to 400+ models, so you can pick one trained on legal language. The platform lets you swap models per step, so you can use a retrieval-optimized model in your first step and a generation-optimized model in your second step.
The practical approach: start with a solid general model in both steps, test your results, then swap the retriever first if accuracy drops. Most of the time, the generation model matters more for user satisfaction, but the retrieval model matters more for accuracy.
I went through this exact decision paralysis last year. The thing I realized is that retrieval and generation are actually different problems. For retrieval, you’re basically doing semantic search—you need a model that understands meaning and can compare similarity. For generation, you need a model that can write fluently and understand context.
What helped me was looking at the specific use case. If you’re doing customer support, Claude tends to perform really well at both steps because it understands nuance. If you’re doing technical analysis, GPT-4 handles complex reasoning better. But honestly, the gap between a good model and a mediocre one for retrieval is smaller than for generation.
Start with whichever model your team already trusts, run a few test queries, and see if it misses relevant documents. If retrieval is fine but responses feel generic, that’s your generation step failing. Swap that first. Most teams overfocus on picking the perfect retriever when the generator is actually what makes responses feel smart and relevant.
I had similar concerns about model selection. The reality is simpler than it seems. Retrieval models need strong semantic understanding—they’re doing similarity matching at their core. Generation models need fluency and reasoning. Most of the top-tier models handle both reasonably well, so you won’t break anything by picking standard choices initially.
Domain-specific models genuinely help, especially for specialized fields like legal or medical. The semantic space is different, and a model trained on that domain understands the subtle distinctions better. If Latenode offers domain-specific variants, they’re worth trying on real queries to see if relevance improves.
The key insight: you don’t need perfect optimization immediately. Build with reasonable defaults, measure actual performance on your data, then iteratively swap models. This beats theoretical optimization without testing.
Model selection for RAG pipelines requires understanding the functional role of each step. Retrieval benefits from models with strong embedding capacity and semantic understanding—this determines which documents get selected. Generation requires fluency, reasoning, and context handling.
With 400+ models available, the decision framework should be: match model strengths to task requirements, test on representative queries, and measure performance using relevant metrics. Domain-specific models provide measurable advantages when the training distribution aligns with your data.
Practically, start with established high-performers (GPT-4, Claude Sonnet, Gemini) for both steps. If retrieval accuracy is acceptable but generation quality needs improvement, prioritize optimizing the generator. The majority of RAG performance gains come from generation quality when retrieval is reasonably accurate.
Retrieval needs semantic understanding, generation needs fluency. Start w/ Claude or GPT-4 for both. Domain models help if available. Test on real queries, then swap whichever step underperforms. Don’t overthink it.