One thing that initially excited me about Latenode was having access to 400+ AI models with one subscription. But having that many options for a RAG pipeline created decision paralysis. I need models for retrieval and generation, but the selection process became overwhelming.
I started experimenting: OpenAI for generation (obvious choice), but then what about the embedding model for retrieval? There’s multiple options there. Same with the re-ranker if I add one. I picked somewhat randomly and got decent results, but I don’t actually know if I made good choices.
Here’s what I’m really asking: is there a framework for thinking about model selection in RAG that doesn’t just come down to trial and error? Like, do embedding models matter as much as generative models? Should I prioritize cost or accuracy? And when you have dozens of models that could technically work, how do people actually decide?
Happy to hear about your approach, even if it was just trying things until something worked.
Having 400+ models is powerful, but you’re right that it requires a decision framework. Here’s what actually matters for RAG:
For retrieval embeddings, consistency matters more than brand. Pick an embedding model and stick with it unless accuracy is bad. The documents are embedded with one model, queries with the same model. Switching models means re-indexing everything.
For generation, you have more flexibility. This is where you can experiment. Larger models like GPT-4 give better outputs but cost more. Smaller models like Mistral are cheaper and often good enough for factual generation from retrieved context. For your domain, one or two tests usually shows which works best.
Ranking is the variable that’s easy to miss. If you add a re-ranker, it’s often worth using a specialized model for that step rather than trying to do it with your main generative model.
The practical approach: start with a solid embedding model and a mid-tier generative model. Test output quality. If it’s good, you’re done. If retrieval is the problem, try a different embedding model. If generation quality is the issue, swap the generative model. This iterative approach beats trying to optimize everything at once.
Latenode makes this testing loop seamless—swap models in the UI and re-run. See the full model ecosystem at https://latenode.com.
I went through this same thing. What helped was separating concerns. The embedding model for retrieval is chosen once and stuck with—it’s foundational. The generative model is where you iterate. I started with GPT-3.5 for cost reasons, tested it, and found it was generating generic answers. Switched to GPT-4 and quality jumped. Cost increased but accuracy justified it for my use case.
The framework I use now is: test with a mid-tier option first. If results are good, done. If retrieval is the bottleneck, the problem isn’t the generative model. If generation is weak, test a different generation model. This narrows the search space from 400 models to maybe 5-10 real experiments.
Model selection in RAG workflows requires understanding the role each model plays. Embedding models determine retrieval quality—they map documents and queries to the same semantic space. Consistency is critical; changing embedding models requires re-indexing. Generative models determine answer quality—they synthesize retrieved context into coherent responses. Testing generative models against your specific data is essential because performance varies by domain. Start with established models and benchmark against your data. Cost considerations are valid; evaluate whether premium models justify their incremental accuracy gains for your use case.
Model selection in RAG follows a hierarchical optimization pattern. Embedding model selection is foundational and stable—switching requires full index reconstruction. Generative model selection is iterative and flexible. Domain-specific performance varies significantly across models; empirical testing against your data is necessary. Larger models provide superior coherence but increase latency and cost. Smaller models offer speed and economy at potential quality cost. Cost-performance optimization requires benchmarking against your specific retrieval and generation requirements.
embedding model is foundational, don’t switch. test generative models on your data. start with mid-tier, adjust based on results.
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