Evaluating RAG Systems with a Small Golden Dataset
Retrieval-augmented generation (RAG) is easy to demo and surprisingly hard to evaluate. A fluent answer can hide weak retrieval, while a good retriever can be blamed for an answer model that ignores its evidence.
A useful evaluation process separates those failure modes. You do not need thousands of examples to begin. A carefully maintained golden dataset of 30 to 100 representative questions can catch many regressions before users do.
Define what the system is supposed to do Start with the product contract rather than a model metric. For a documentation assistant, useful requirements might be:
