This is the problem I keep running into. Latenode gives you access to all these models—OpenAI, Claude, Deepseek, specialized retrieval models—and the first time I looked at the list, I just froze.
For RAG specifically, you need to pick a retriever and a generator. That’s already a decision. But when you have hundreds of options, how do you actually choose?
I realized I was overthinking it. Started building a workflow and just picked what I knew: Claude for generation because I’ve had good results with it. For retrieval, I grabbed a simpler model because the retrieval step doesn’t need to be fancy—it just needs to pull relevant data from the knowledge base.
Then I actually tested different combinations. Cheap retriever with expensive generator. Both expensive. Both cheap. Turns out cheap retriever + Claude worked fine for my use case. The retrieval quality mattered less than I thought because the generator could work with imperfect data.
What I learned is that you don’t need to analyze all 400 options. You need to understand your tradeoff: speed versus cost versus quality. Then pick models that fit that profile.
For retrieval, I realized smaller models often work fine because retrieval is mostly pattern matching. For generation, larger models handle nuance better but cost more. That’s usually the choice.
Some of my colleagues just picked models based on pricing and called it done. Others tried to optimize each step independently and spent weeks tuning. I landed somewhere in the middle—picked reasonable defaults, tested quickly, and moved on.
How are you guys actually making this decision? Are you treating it as a one-time optimization problem or more of a continuous thing where you swap models as you learn what works?
The key is treating it as a constraint satisfaction problem, not an optimization problem.
Your constraints are usually: speed, cost, and quality. You pick a point in that space and then verify it works. If it doesn’t, you move the needle on one constraint.
For RAG, I default to: cheap retriever, better generator. That’s usually the right call because retrieval is mostly mechanical and generation is where quality matters. Then I test.
What makes Latenode useful here is that swapping models is literally one click. So you can test different combinations easily. Try Claude, try GPT-4, try whatever. See what works for your actual data and actual use case.
I built a support automation workflow and cycled through four different generators before landing on one that handled our specific response style well. With traditional tools, that would’ve meant code changes each time. Here it was visual swaps and testing.
The 400+ models thing stops being overwhelming when you realize you’re probably going to use 2-3 models max. The rest are just optionality for edge cases.
I approached it empirically. Built the workflow with reasonable defaults—didn’t overthink the model selection. Then I measured: latency, cost per request, output quality for my specific use case.
Based on measurements, I tweaked. Sometimes that meant paying more for a better retriever. Sometimes it meant optimizing retrieval parameters instead of changing models. The data pointed the direction.
The mistake I see people make is trying to choose the perfect model upfront. But you don’t know what perfect means for your use case until you test. Pick something decent and iterate based on real measurements.
Model selection follows from requirements. For retrieval, prioritize relevance and speed. For generation, prioritize coherence and tone. Most use cases succeed with mid-tier models for retrieval and better models for generation. Avoid trying to optimize all axes simultaneously.