Your RAG problem is an eval problem, not a vector-store problem.
Teams burn months optimizing chunk sizes, hybrid retrieval, and reranker stacks. Then they ship — and discover they never had a reliable way to know if the answer was right.
Most RAG systems we are asked to rescue have a sophisticated retrieval pipeline and a non-existent eval harness. The team can describe their reranker but cannot tell you the F1 on a held-out set of expert-graded answers.
This is backwards. The eval harness is the spec. Without it, every change is a vibes-based experiment — chunk size up, chunk size down, embedding model swap, hybrid weight knob — with no way to know if you are getting better or worse.
Build the eval first. Hand-grade fifty representative queries with expert ground truth. Score every change against that set. The retrieval architecture you end up with will be different — and shippable.
Frequently Asked Questions
Why is a RAG problem usually an eval problem, not a vector-store problem?
Because most struggling RAG systems have a sophisticated retrieval pipeline and no reliable way to know whether an answer is right. Without an eval harness, every change — chunk size up or down, embedding-model swap, hybrid-weight knob — is a vibes-based experiment with no signal on whether it is getting better or worse.
How should teams fix an unreliable RAG system?
Build the eval first: hand-grade about fifty representative queries with expert ground truth, then score every change against that set. The eval harness is the spec, and the retrieval architecture you converge on will be different — and actually shippable. Enso Labs builds eval harnesses as the foundation for production RAG; get in touch at https://ensolabs.ai/contact.
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