AI EVALUATION · RESEARCH OPERATIONS
Measure AI-assisted research before relying on it
An evaluation harness that turns qualitative review of AI-assisted research into repeatable metrics, claim-level evidence checks, and failure analysis.
Evaluation problem
AI-assisted research is useful only when quality can be measured and failure modes can be diagnosed. The framework treats a generated response as an analytical artifact that should be auditable and explicit about uncertainty.
Workflow
AI output → Claim extraction → Evidence check → Accuracy → Completeness → Traceability → Cost/latency → Failure taxonomy.
Metrics
- Claim-level precision, recall and F1.
- Completeness.
- Citation coverage.
- Consistency.
- Latency.
- Cost.
- Failure categories.
Outputs
- Gold-standard evaluation set.
- Claim-level scorecard.
- Completeness and citation checks.
- Latency/cost tracking.
- Failure taxonomy.
- Evaluation report.
Economics and statistics benchmark
A complementary ten-item starter set tests numeric calculation, exact short answers, and open-ended reasoning. Numeric and exact-match items are scored deterministically; open-ended answers are marked for human review. The sample response file is explicitly labelled as placeholder data and is not an evaluation of a deployed model.
Limits
The fixture uses synthetic labels and does not measure a deployed AI system. Production evaluation would require representative workloads, versioned references, monitoring, and human review for consequential outputs.