How to test construction AI before it runs real workflows: build a test set, measure accuracy and false confidence, and set release thresholds.
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Measure factual accuracy, completeness, source grounding, format compliance, false confidence, exception handling and whether a qualified reviewer can efficiently verify the result.
| CATHA PRACTICAL AI FOR CONSTRUCTION LIBRARY Trust, But Test Construction AI How to Evaluate AI Before It Runs Construction Workflows Guide 8 |
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| Best use Build a test set before production. Measure success, failure and uncertainty against ground truth and business consequences. |
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| Layer | What to test |
|---|---|
| Model/output quality | Accuracy, completeness, relevance, groundedness |
| Workflow quality | Correct routing, tool selection, state handling |
| Safety/control | Permissions, prohibited actions, escalation |
| Operational reliability | Latency, retries, duplicate prevention, outage behavior |
| Economic performance | Cost per successful outcome, human review burden |
| Human usability | Clarity, trust, adoption, correction effort |
| Metric | Pilot threshold | Production threshold |
|---|---|---|
| Critical factual accuracy | Define | Define |
| Unsupported claims | Define max | Lower max |
| Correct escalation | Define | Near-100% for high-risk cases |
| Duplicate external actions | 0 | 0 |
| Unauthorized actions | 0 | 0 |
| Human correction time | Target | Target |
| Cost/successful case | Target | Target |
| Check | Item | Notes |
|---|---|---|
| ☐ | Test suite reruns after prompt changes | ____________________________ |
| ☐ | Rerun after model changes | ____________________________ |
| ☐ | Rerun after tool/API changes | ____________________________ |
| ☐ | Compare new vs. prior version | ____________________________ |
| ☐ | Block release on critical regressions | ____________________________ |
| ☐ | Retain failing examples for future tests | ____________________________ |
| Golden rule If a system can create consequential business effects, evaluation must test the full workflow – not just whether the model writes a convincing answer. |
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Practical questions construction owners ask.
What should a construction AI evaluation measure?
Factual accuracy, completeness, source grounding, format compliance, false confidence, exception handling and whether a qualified reviewer can efficiently verify the result.
When should the test set be built?
Before production. Measure success, failure and uncertainty against ground truth and business consequences, and rerun the tests after prompt, model or tool changes.
Which thresholds should never be exceeded?
Duplicate external actions and unauthorized actions should both be zero, in the pilot and in production.
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