Start with the question behind the score
My RAG project reports an average judge score of 3.6 out of 5 across ten queries. Six answers scored four or five. Those numbers describe a small run; they do not establish how the system performs across the questions a future user might ask.
The useful question is what the run helps diagnose. The system searches a corpus of 73 research papers, reranks retrieved candidates, and logs the answer, latency, and judge score. Keeping those pieces together makes an answer something I can investigate rather than simply accept.
Separate missing evidence from a weak answer
The repository reports low scores for questions on topics underrepresented in the corpus. That points to a coverage problem worth inspecting. A low final score alone cannot tell me whether to add documents, change retrieval, adjust chunking, or revise generation.
For the next evaluation, I would record whether the corpus contains an answer, whether retrieval finds it, and whether the final response uses it correctly. An answer can fail at any of those boundaries. A single aggregate score hides that distinction.
Make the result reproducible
The next useful artifact is a versioned set of per-query records: question, expected evidence, retrieved passages, response, judge reasoning, and latency. It should travel with the corpus version, judge rubric, and model settings. The current portfolio labels the aggregate numbers as repository-reported because the raw ten-query results are not yet committed.
I would then expand the set with answerable, unanswerable, and ambiguous questions and add human review. The reported 950 ms average also needs a specified environment and a latency distribution before it becomes useful as an operating target.
The lesson
A small evaluation is valuable when it identifies the next experiment. For this project, the next step is a better evidence trail and broader coverage. The dashboard becomes more useful when a reader can move from a number to the exact run that produced it.