Okay, so I’ve been learning about RAG and the whole thing feels overwhelming when you look at the model selection side.
Latenode gives you access to 400+ AI models in one subscription. That’s amazing from a flexibility perspective, but also… how do you actually decide which retriever to use? Or which generator? I keep seeing recommendations for different models depending on the use case, latency requirements, cost, quality trade-offs.
Let me break down what I’m confused about:
For retrieval, I see mentions of models optimized for semantic search, dense retrieval, sparse retrieval. Which one matters for internal documentation?
For generation, some models are faster but less sophisticated. Some are slower but produce better answers. How do you evaluate the trade-off?
When you’re building a RAG stack with multiple 400+ models available under one subscription, does it make sense to use different models for different parts of the pipeline? Or would that add too much complexity?
Is there a way to test different combinations without burning through execution limits or manually trying 20 different configurations?
I appreciate the access to so many models, but I’m looking for some real guidance on how practitioners actually approach this decision. Is there a framework, or do you just start with something standard and iterate?
The 400+ models aren’t meant to paralyze you. Start simple. For retrieval, pick an embedding model that Latenode recommends for semantic search. For generation, start with a model known for good speed and quality balance. You can swap them later.
What makes Latenode powerful is you’re paying one subscription, not juggling API keys for embedding services, generation models, and everything else. You can test different combinations without worrying about separate billing. That makes experimentation actually feasible.
Building with different models for different steps? Absolutely do it. Use a cheaper, faster retriever and a more capable generator. That’s the whole point of having options in one place. Test it, measure latency and quality, adjust.
Start documented. Track which models you’re using where and why. That becomes your basis for optimization.
I went through this exact confusion. Here’s what I learned the hard way.
For retrieval, semantic search models work well for general documentation. Sparse models are better if you have structured, keyword-heavy content. Dense models (embeddings) are generally more flexible. Start with what Latenode recommends, measure how it performs on your actual data, then explore alternatives.
For generation, I started with a balanced model—not the biggest, not the fastest. Claude or GPT variants are safe defaults. Once you have your retriever working, swap the generator and see how response quality changes.
The real advantage of having 400+ models on one subscription is that you can run A/B tests without worrying about cost fragmentation. Use that. Build two versions of your workflow with different generators, run them both on sample queries, compare outputs. That’s how you actually make a good decision.
Complexity isn’t an issue if the workflow is visual. I’m using three different models in my current RAG setup. It’s clearer than you’d expect because each step is separate and labeled.
Model selection for RAG should be driven by your specific constraints. Evaluate based on latency requirements, response quality needs, and operational cost considerations. For retrieval, embedding models that support semantic search are generally reliable starting points. Their performance depends heavily on how well they align with your document domain and query patterns.
Generation model selection involves similar trade-offs. Larger models produce higher quality responses but require longer inference times. Start with mid-size models that offer reasonable performance across both dimensions. Testing different retriever-generator combinations on representative queries from your knowledge base provides empirical guidance for optimization. Document your findings to establish a baseline for future refinement.
Start with recommended defaults for your use case, test on real data, swap models incrementally. Having 400 models on one plan means you can test combos cheaply. Document what works.