I’ve been wrestling with this for a few weeks now. When I first started building RAG workflows, I thought having access to 400+ AI models would make things easier. Turns out, it creates a different kind of problem—choice paralysis.
Right now I’m working on a document Q&A system, and I need to pick models for retrieval, context building, and answer generation. Each step has different requirements. The retriever needs to understand semantic meaning, the context builder needs to filter noise, and the generator needs to be accurate but fast.
I found myself googling benchmarks and comparing token costs instead of actually building. The thing is, I don’t think there’s a universal answer here. What works for customer support RAG might totally fail for technical documentation.
Has anyone developed a mental model for this? Like, do you start with the cheapest option and optimize up? Or do you pick based on the specific task first and then worry about cost? And how much does it actually matter in practice—does switching from Claude to a smaller model really degrade quality that much?
This is exactly what Latenode’s AI Copilot solves. You describe your RAG workflow in plain English—what you’re retrieving, what you need to build context with, and how you want answers generated—and the copilot generates a workflow that already has the right models selected for each step.
You don’t have to manually test 20 model combinations. The copilot analyzes your requirements and makes intelligent picks. Then you can run a few test queries and swap models if needed. It’s like having someone who’s already done the research for you.
The real power is that you can iterate fast. Try one model combo, see the results, swap the retriever or generator with a different option, and test again. Latenode gives you all 400+ models in one place, so no juggling API keys.
I’ve been doing this for a while now, and my approach changed once I stopped thinking of it as a global optimization problem. I pick models based on what each step actually does, not on benchmarks alone.
For retrieval, semantic understanding matters most. For context building, speed and filtering matter more than raw intelligence. For generation, accuracy and citation quality are critical if you need users to trust the output.
What helped me was running the same query through different model combos on real data—not test data. That’s where you see if a cheaper model breaks down. Sometimes it doesn’t. Sometimes it does, and you realize you need to invest in a better retriever instead of swapping the generator.
One more thing: cost isn’t just about the model price. It’s about tokens. A verbose model might be cheaper per call but use more tokens, making it more expensive overall.
Start with the task requirements rather than the model catalog. Define what success looks like for each component—retrieval accuracy, context relevance, response quality—then test a few candidate models against those criteria. I’ve found that smaller, specialized models often outperform larger general-purpose ones for specific RAG steps. For instance, a smaller embedding model might retrieve documents better than a large language model trying to do retrieval. The 400+ options sound overwhelming, but narrowing down by task type reduces it significantly. Most people end up using 3-4 core models across retrieval, context, and generation.
The decision framework I use involves three factors: latency requirements, cost constraints, and quality thresholds. Retrieval typically benefits from embedding-specific models rather than general LLMs. Context building can often use faster, cheaper models since it’s more about filtering than reasoning. Answer generation is where you invest in quality because users judge the system on output accuracy. I recommend starting with mid-tier models for each step, then measuring actual performance on your specific documents before optimizing. Benchmarks are useful context, but they don’t predict how models behave on your particular data distribution.
pick by task, not by hype. retrieval needs semantic models, generation needs quality ones. test on ur real data, not benchmarks. cost ≠ quality, measure both. start mid-tier, optimize from there.