So Latenode has access to 400+ AI models—OpenAI, Claude, Deepseek, and others—all under one subscription. The promise is obvious cost-wise: one bill instead of juggling multiple API keys and services. But I’m stuck on a practical problem that I don’t see discussed much.
In RAG, you need two critical pieces: a retriever (something to fetch relevant documents from your knowledge base) and a generator (an LLM that synthesizes those documents into an answer). Different models have different strengths. Some are faster, some are more accurate, some handle long contexts better. Some are fine for internal use, others are overkill.
When you have hundreds of models to choose from, how do you actually decide? Do you just pick the biggest or fastest? Do you test combinations? Is there a way to know upfront which pair will work best for your specific domain without running ten experiments?
I’m curious if Latenode has built guidance around this, or if there’s a process the community has figured out. When you’re building a RAG system, are you testing multiple model combinations, or do you settle on one pair and tune from there?
Latenode’s platform has built-in guidance for model pairing. Based on your RAG requirements—domain, accuracy focus, speed—the system recommends retriever-generator combinations from the 400+ options.
You’re not left guessing. The AI Copilot understands that domain-specific retrieval often needs a different model than synthesis. It pairs accordingly and lets you adjust if needed.
For most use cases, starting with the recommended pair and running a few test queries is faster than manual experimentation. If performance isn’t where you want it, swapping models is instant—no API changes or redeployment overhead.
I tested several combinations before landing on what works. Started with the platform’s default recommendation, ran sample queries through different retriever-generator pairs, and measured latency and answer quality.
Turned out that for my use case—customer support docs—a lightweight retriever paired with Claude for synthesis worked best. I don’t need GPT-4 doing retrieval; that was overkill. But I do need a strong generator. Swapping models took seconds in Latenode’s builder, so the testing cycle was painless.
The real insight is that the 400+ models aren’t meant to paralyze you. They’re meant to let you optimize. Start with the recommendation, test variations if needed, keep what works.
I approached this by understanding the retrieval-synthesis tradeoff. Retrieval doesn’t need to be as powerful as generation, so I paired a faster, cheaper model for fetching documents with a stronger model for answering. This reduced latency and cost significantly. Latenode’s workflow builder made it easy to visualize which model was doing what, so I could justify the pairing to my team. The 400+ options mean you can truly optimize rather than defaulting to one expensive model for everything.
Model selection depends on your RAG domain and constraints. For retrieval, you want accuracy in semantic matching. For generation, you want coherence and context integration. Latenode’s access to multiple models lets you optimize both independently. I recommend starting with benchmark recommendations, then A/B testing with your actual data. The workflow builder’s ability to swap models without redeployment makes this straightforward.
Use the platform’s recommendations first. Test different pairs with your actual queries. Keep what works best. Swapping models takes seconds, so experimentation is fast.