Scientific RAG Needs Evaluation Before Confidence
RAG systems for technical work should be judged by retrieval quality, citation faithfulness, and decision usefulness, not only fluent answers.
In scientific domains, a fluent answer is not enough. The answer has to be tied to evidence, and the retrieval step has to be good enough for the decision being made.
For a scientific RAG system, I care about three evaluation layers:
- Retrieval quality: did the system find the right papers, records, entities, or experimental facts?
- Citation faithfulness: does the generated answer actually follow from the cited context?
- Decision usefulness: does the answer help the user choose a next step?
These layers are different. A system can retrieve the right source but summarize it poorly. It can generate a clear answer but cite weak evidence. It can be factually correct but not useful for the decision at hand.
This is why I prefer to build RAG workflows with trace inspection and small smoke tests early. Before making the interface polished, the system should make failure visible: missing sources, weak matches, stale records, or overconfident summaries.
ScientificLoop is an evolving place to test that pattern publicly: small corpus, clear boundaries, cited answers, and workflow-oriented navigation.