I’m building a RAG pipeline and I’ve hit this fuzzy point where I’m not sure how to measure if it’s actually good. You can get answers back, but that doesn’t mean they’re useful. How do you actually know if your retrieval is finding the right documents? How do you know if your generation is accurate?
I’ve heard people mention metrics like precision and recall, but honestly those feel like academic concepts. In the real world, when you’re trying to justify RAG to your team, what metric actually proves it’s working?
Does anyone have a framework for this that doesn’t require spending a week on evaluation setup?
The honest answer is that the metric depends on your use case, but there’s an easy starting point: just compare your RAG answers to what a human would say. If 8 out of 10 answers are helpful, you’re doing okay. If it’s 3 out of 10, something’s broken.
In Latenode, you can run your pipeline on a batch of test queries and manually grade the results. It’s not fancy, but it works. Once you see the pattern—like maybe retrieval is pulling the wrong documents—you can fix the actual problem instead of guessing.
For more rigor, tools like RAGAS automate parts of this evaluation, but they require setup. Start manual, understand your failure modes, then automate.
Most people overthink evaluation. Start with a simple question: does the generated answer actually answer the user’s question using information from your documents? Test that on 20-30 real queries. Count how many succeed. That’s your baseline. Then identify the failure modes—is retrieval pulling wrong documents, or is generation hallucinating? Fix the obvious problems first.
Precision and recall matter if you’re building a search engine, but for RAG, user satisfaction is more important. Does the answer help? That’s the real metric. Precision at retrieving documents doesn’t help if the generation step hallucinates anyway. Focus on end-to-end correctness.
start simple: test 20 queries, manually grade answers as correct or not. that’s ur baseline. then identify where it breaks—retrieval or generation. fix those problems. automate later if u need.