I’ve been looking at Latenode’s model catalog, and having 400+ models available is both powerful and paralyzing.
Like, I understand that different models are good at different things. Some are fast but less accurate. Some handle specific domains better. Some are cheap, others are expensive.
But how do you actually decide? Do I spend weeks benchmarking every model combination? Do I just pick what’s popular? Do I look at cost first or quality first?
For RAG specifically, I’m wondering if retrieval and generation models need to be paired in specific ways. Does a model that’s great for retrieval necessarily play well with a particular generation model? Or is it mostly independent?
I want a mental model that helps me make decisions without overthinking it. Something like “start with this, then optimize based on these criteria.” Has anyone actually done this and landed on a process that works? What’s your decision framework?
The good news is that model selection doesn’t need to be as complex as it seems.
For RAG, retrieval and generation have different jobs, so they need different qualities. Retrieval models need to understand semantic meaning and pull relevant documents. Generation models need to synthesize information and write coherently.
Here’s a practical approach: start with proven defaults. Embedding models like OpenAI’s ada work well for retrieval. For generation, GPT-4 or Claude are solid if you have budget, or use faster options like Mistral for cost-sensitive use cases.
Then run tests. Feed your actual data through the retriever, see what documents it pulls. Try different generators on those documents, see which produces better answers. You’ll quickly find what works for your specific data.
The beauty of having 400+ models in one subscription is that you’re not locked in. You can test multiple models without juggling API keys and billing. That flexibility itself simplifies the decision.
Don’t overthink initial selection. Good enough now beats perfect later. You can always improve.
I spent way too much time on this at first. Here’s what I learned: retrieval and generation models are mostly independent.
Retrieval quality matters a lot. If you’re fetching irrelevant documents, a great generator can’t fix that. So pick a retrieval model that understands your domain. For specialized content like legal docs or technical specs, domain-specific embeddings might help.
Generation is about output quality and speed. If you need fast responses, pick a faster model. If you need nuanced answers, go bigger.
Cost is real too. If you’re running high volume, those costs add up. I found that for many use cases, a mid-tier retrieval model plus a cost-efficient generator worked as well as going top-tier on both.
The process I settled on: pick reasonable defaults, run 100 test queries with your real data, measure accuracy. Then decide if you need to upgrade or if current models are fine.
Your mental model should separate retrieval quality from generation quality. They’re different problems.
Retrieval is about completeness and relevance. You want the right documents in your result set. Generation is about turning those documents into useful answers.
I’d recommend starting with a standard embedding model for retrieval—they’re pretty commodity now and work well across domains. For generation, pick based on your output requirements. Need fast? Use something lean. Need sophisticated reasoning? Go bigger.
Then benchmark against your actual use cases. What retriever accuracy do you need? What generation quality is acceptable? Once you know those thresholds, model selection becomes clearer.
Avoid obsessing over picking the perfect model combination upfront. Real data usually tells you pretty quickly what works.
Model selection benefits from decomposing the decision criteria. For retrieval, optimize for relevance and recall within your domain. For generation, balance latency requirements with output quality expectations.
A useful framework: cost-quality matrix. Plot candidate models by cost per inference against observed quality metrics from your test set. Models in the upper left (cheap, good) are often your best choices.
Retrieval and generation models interact indirectly through document quality. Poor retrieval limits what even excellent generators can accomplish. This cascading dependency suggests focusing optimization effort on retrieval first.
Start with proven models (OpenAI ada for retrieval, GPT-4 or Claude for generation). Test with your data. Optimize based on cost and quality. Retrieval and generation are mostly independant, so fix the worst one first.