I’ve been experimenting with building a RAG workflow and one thing that’s driving me crazy is that I can plug in different models at every step—embeddings, retrieval, reranking, generation—and the results change in ways I don’t fully understand.
Everyone talks about accuracy, but that’s way too vague for what I’m trying to do. Am I measuring whether the right document gets retrieved? Whether the ranking order is correct? Whether the final answer sounds good to a human? Those are three completely different things.
I watched this legal firm case study where they used RAG for contract analysis. They mentioned 75% reduction in processing time, but I have no idea if that was because retrieval was faster, the AI model was more efficient, or they just didn’t count refinement time.
What I’m really trying to figure out is: when you’re iterating on model choices, are you measuring retrieval quality independently from generation quality? Or are you looking at the entire pipeline as one thing?
I know Latenode gives you access to 400+ models, which theoretically means unlimited combinations to test. But that’s also completely overwhelming. How do people actually decide which model to try next when something isn’t working?
What metric do you actually track when you’re optimizing a RAG workflow?
You need to measure retrieval and generation separately, then track end-to-end performance.
For retrieval, measure precision and recall on your actual queries. Does the right document get in the top-3 results? That’s your retrieval metric. For generation, measure relevance and hallucination—does the final answer accurately reference what was retrieved?
End-to-end, measure what matters to your business. For support bots, it’s resolution rate and customer satisfaction. For document analysis, it’s accuracy on a test set you care about.
Here’s the practical approach: start with one model for each step. Run 20-30 test queries. Measure where things break. If retrieval is the problem, swap the embedding model. If generation is loose, swap the language model. Change one variable at a time.
With 400+ models available in Latenode, you’re not actually testing combinations. You’re testing which model solves your specific bottleneck. That’s how people make progress instead of getting lost in options.
I’ve seen teams test 5-6 model variations and converge on the right setup in a week. The key is measuring each step separately so you know what to optimize next.
I separate them in my head this way: retrieval quality is about finding the right context. Generation quality is about using that context well. If your answer is bad, you need to know which one failed.
I set up a test harness that logs what document was retrieved, what was generated, and whether a human would call the answer acceptable. Over 50 queries, patterns emerge fast. Maybe retrieval is missing documents 20% of the time, or generation is adding information not in the source.
Once you know the problem, model swaps are quick experiments. Switched embeddings three times testing different one until retrieval improved. Switched the generation model twice.
What actually matters depends on your use case, but I measure both separately because they scale independently.
Retrieval and generation should be measured independently. For retrieval, measure whether the correct document appears in results and in what rank position—this tells you if your embedding and retriever model are doing their job. For generation, measure whether the model synthesizes retrieved information accurately without adding hallucinations.
End-to-end, measure what your actual use case requires. If a customer support bot, measure whether the answer resolves the issue. If research assistance, measure whether the answer is factually accurate and sources are referenced.
The mistake I see is people treating RAG as a black box. When performance is mediocre, you need to know which component is failing so you don’t waste time swapping models that aren’t the bottleneck.
Decompose the problem. Retrieval metric: recall at k—does the relevant document appear in your top results? Generation metric: BLEU score or semantic similarity to reference answers. End-to-end metric: whatever success looks like in production context. This separation prevents conflating issues. Most suboptimal RAG systems fail because people swap generation models when retrieval is actually the problem.