I’ve been diving into RAG workflows lately and something that keeps tripping me up is this: when you’re setting up retrieval and generation in a RAG pipeline, how do you actually choose between all these models?
Like, I know retrieval needs to be fast and accurate at finding relevant information from your sources. And generation needs to craft a coherent answer from that context. But with 400+ models available in one subscription, does it really matter which one you pick for each job?
I’ve seen people use the same model for both. I’ve seen others swap between Claude for generation and a smaller model for retrieval. The retrieved context mentions you can choose the best AI model for each specific task and use prompt engineering to optimize, but that feels kind of vague when you’re actually building the thing.
Is there a practical difference in results, or is it more about cost optimization? And if you do split them up, how do you even test if one combination is better than another?
I ran into this exact same issue when I was setting up a RAG system for internal documentation. Here’s what I learned: it really depends on your use case.
For retrieval, you want something that understands semantic meaning well but doesn’t need to be massive. Smaller models actually often work better here because they’re faster and you’re just matching documents, not generating complex output.
For generation, you want something more capable because it needs to synthesize information and write something coherent. That’s where I’d lean toward Claude or GPT-4 if your budget allows.
The thing that changed my approach was realizing Latenode lets you test this visually without burning through API costs on each model. You can wire up different model combinations in the builder, run them against your actual data, and see which gives you better results. Take a screenshot of the response quality, response time, and costs. Do that a few times with different combos.
Also, Latenode’s prompt engineering tools help you optimize what you’re sending to each model. A well-tuned prompt to a smaller model often beats a lazy prompt to a huge one.
The real answer: test it with your actual data and your actual questions. Generics won’t help you here. That’s what I do now, and the difference is noticeable.