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Paper
A Lightweight Framework for Evaluating LLM Assistants in Production
A practical, low-cost evaluation loop that small product teams can run on every release of an AI feature.
Abstract
We propose a lightweight evaluation framework for teams shipping LLM-based assistants without a dedicated ML team. It combines a curated question set, rubric-based grading and production feedback into a loop that runs on every release.
Key findings
- A small curated set of real user questions catches most regressions.
- Rubric-based grading is more stable than single-score grading.
- Production thumbs-up/down feedback is the best source of new test cases.
Motivation
Most teams ship AI features by "vibe checking" a few prompts. That works until it doesn't — a model upgrade or prompt tweak silently breaks answers users relied on.
The loop
- Collect — gather real user questions and the answers you'd want.
- Grade — score each answer against a short rubric: correct, grounded, helpful, safe.
- Compare — run every change against the set and compare with the last release.
- Learn — turn production feedback into new test cases.
Rubric example
| Criterion | Question |
|---|---|
| Correct | Is the answer factually right? |
| Grounded | Is it supported by the retrieved sources? |
| Helpful | Does it solve the user's problem? |
| Safe | Does it avoid actions or claims it shouldn't make? |
Conclusion
Evaluation doesn't need to be expensive to be useful. It needs to be consistent.
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