so i’ve been playing around with rag workflows in latenode and i keep hitting this wall where i have to pick models for different parts of the pipeline. like, you’ve got retrieval on one end and generation on the other, and the platform gives you access to like 400+ models. in theory that sounds amazing, but in practice i’m kind of drowning in options.
i started by just using the same model for both parts because it felt simpler, but that doesn’t feel right. some models are probably way better at understanding what you’re searching for versus actually writing coherent answers. then i realized the retrieval side is almost like a search problem - you need something that understands semantic meaning, not necessarily something that’s great at prose.
igeneration side is different. you want something articulate, something that can actually string thoughts together in a way that makes sense to whoever’s reading it.
the thing that threw me is that with 400+ models available, there’s no simple “use this one” guide. latenode’s documentation mentions choosing appropriate models for each task and implementing proper prompt engineering, which is helpful but also kind of vague when you’re actually standing in front of the model selector.
have any of you found a pattern that actually works? like, do you stick with one high-performing model and just use it everywhere, or do you genuinely split the work across different models?
this is where latenode’s ai copilot workflow generation actually saves you hours of second-guessing. instead of manually testing combinations, you describe what you need - “i need to search customer documentation and return helpful answers” - and the copilot builds the workflow with model recommendations baked in.
what i see work best in practice is using a focused retrieval model like a specialized embedding model for the search layer, then something like claude or gpt-4 for generation because they handle context really well. but honestly, the beauty of having 400+ models is you can experiment without API key headaches.
in my experience, the retrieval model matters less than people think if your prompts are solid. what matters more is whether your knowledge base is properly structured. generation is where model choice shows up immediately - users notice if answers sound robotic or miss nuance.
latenode lets you swap models in seconds without touching api configurations, so you can actually test this theory without breaking anything. that’s the real win.
i went through exactly this problem with a customer support workflow. started thinking i needed the fanciest model for both layers, which was overkill and wasteful.
what actually worked was stepping back and asking what each part actually does. retrieval just needs to understand intent and find relevant documents - it’s a matching problem. i ended up with a smaller, faster model there because latency matters when you’re doing multiple retrievals.
generation is where users actually experience quality, so that’s where the more capable model makes sense. users notice if an answer is generic or missses their specific context, but they don’t care how fast retrieval happens as long as it’s under a second or two.
the other thing i learned is that prompt engineering changes everything. sometimes a well-tuned prompt on a mid-tier model beats a lazy prompt on the biggest model. latenode makes this experimentation smooth because you’re not juggling oauth tokens and rate limits.
start with something reasonable, run it, see where it breaks, then optimize from there instead of overthinking it upfront.
the trap i fell into was assuming more powerful always meant better results. with 400+ models available, you’d think you’d want the absolute best for everything, but that’s not how it actually works in the real world. retrieval and generation are fundamentally different tasks. retrieval is about matching intent to content, while generation is about expressing that content clearly. i found that mixing a specialized retrieval model with a strong generation model gives better results than using the same powerful model everywhere. the key insight is that you’re not competing on raw power - you’re competing on how well each model handles its specific job. latenode’s environment makes this testable without friction because you’re not managing separate api keys and rate limits for each model.
split the work. use a focused retrieval model for search accuracy, then a stronger generation model for answer quality. prompt engineering matters more than raw model size. test combinations quickly in latenode without api friction.