Testing strategy for AI on Oracle
AI that reads your data and acts on it needs more than functional testing. We test the answers, the access, the approvals and the cost, and repeat the tests after every Oracle patch or model change.
What we test
Accuracy
Answers and extractions are scored against a labelled set drawn from your own data.
Access
Tested as each Oracle role, so the AI returns only what that user may see.
Approvals
Every write needs a yes. We try to bypass it, and it must hold.
Validation
Generated SQL, ROQL and CDP queries are checked against the real schema before they run.
Confidence
Thresholds decide what is acted on and what goes to a person.
Cost
AI cost per run and per transaction is measured, not assumed.
Our process
- Define successPick one process. List the questions and documents the AI must handle, and set targets for accuracy, confidence and time saved.
- Build a labelled test setReal questions, invoices or tickets from your environment, each with a known right answer.
- Test as each roleSign in as users with different responsibilities and confirm the AI returns and changes only what each may.
- Test the guardrailsAttempt writes without approval, malformed and adversarial prompts, and failed checks. Confirm that it fails closed and that sensitive data never reaches the model.
- MeasureAccuracy, confidence against correctness, time saved, user feedback and AI cost per transaction.
- Re-test on every changeRun the regression pack after Oracle quarterly patches, model changes and prompt changes, using our automation framework.
What you receive
- Test reportResults by scenario against the targets you set.
- Role-access resultsWhat each role could and could not do.
- Guardrail resultsApprovals, validation and fail-closed behaviour.
- Cost per transactionThe actual AI cost, so finance can plan.
- Regression packA repeatable suite for the next patch or model change.
Test AI before it touches production
Book 30 minutes at Oracle AI World to review your use case and a test plan.