Having access to 400+ AI models through one subscription sounds amazing until you actually need to pick which embedding model and which LLM to use for retrieval vs generation. I’ve been stuck in decision paralysis more than once.
The theoretical answer is “test them all.” The practical answer is that testing takes time, and I need to actually deploy something. So I’ve been trying to figure out what actually matters when comparing models.
For retrieval, embedding quality matters most. I tested a few embedding models and noticed the difference between them comes down to how well they understand your specific domain. A generic embedding trained on broad data might not capture the nuance in your knowledge base.
For generation, I’ve found that smaller, focused models sometimes outperform bigger ones if they’re tuned well. The retrieval quality matters way more than generation model size, which surprised me.
What I’m doing now is picking two or three candidates for each step based on reputation and use case match, testing them with a small sample of real queries, and measuring retrieval precision and generation quality. Not exhaustive, but practical.
But I’m curious—when you’re testing models, what’s your actual process? Are you using some kind of evaluation framework, or just running it and seeing what feels right?
The beauty of testing in Latenode is you can swap models in a workflow without rebuilding anything. You’re not choosing once and living with it—you can test, measure, and adjust.
You mentioned evaluation frameworks, and that matters. Running queries and “feeling” which answer is better isn’t reproducible. Tools like RAGAS give you metrics—retrieval precision, generation faithfulness—that let you compare models objectively.
The paralysis disappears when you realize you don’t need the single best model. You need a good model that works for your data. Test a handful of options, pick the one that performs well on your evaluation set, and move on. You can always switch later.
With one subscription covering hundreds of models, testing iterations cost almost nothing. That’s the actual advantage—you can be intentional about model selection without worrying about API costs exploding.
Your instinct about testing a subset is right. Testing every combination is infinity. I pick a few candidates based on what others have used successfully for similar problems, then test those against a sample of my actual data.
For retrieval, the embedding model matters way more than most people realize. I tested OpenAI’s embedding against a few open source options and the difference in how well they understood our product documentation was significant. Spend time on that choice.
For generation, you’re right that bigger isn’t always better. Smaller models tuned well beat larger generic models. I’ve gotten good results from smaller Claude models that are faster and cheaper than the biggest options.
Model selection in RAG systems requires structured evaluation because subjective assessment doesn’t capture real performance. Your approach of testing a curated set against representative queries is sound. Evaluation should measure retrieval precision—how often the retriever returns relevant documents—and generation quality against a ground truth answer set. This prevents selection bias where a model produces answers that feel right but hallucinate details. The distinction you noted between embedding and generation model importance is accurate. Embedding quality directly impacts retrieval precision, which constrains generation quality. A good embedding model with an average generation model often outperforms poor embeddings with an excellent generation model.
Model selection should be informed by empirical evaluation on your specific data distribution. Generic benchmarks don’t predict performance on domain-specific retrieval tasks. Testing methodology matters—evaluate embedding models on retrieval precision using your actual knowledge base, and evaluate generation models on factual consistency and relevance using your ground truth answer pairs. The constraint you identified about testing scope aligns with practical deployment schedules. A stratified sampling approach where you test models on 50-100 representative queries provides sufficient signal for selection decisions. The observation that embedding quality constrains generation quality reflects the RAG pipeline bottleneck principle—the weakest component in the pipeline determines overall system performance.
test 3-5 candidates per step on real data samples. embedding quality matters more than generation model size. use metrics, not gut feeling. move on after testing.