AI model test and evaluation plan
How to test an AI model or agent: test sets, metrics, thresholds, adversarial tests and sign-off.
AI Test and Evaluation Plan
1. What is tested
Model/agent, prompts, tools it can call, Oracle integrations.
2. Test data
Golden set of real, labelled examples (minimum 100–300), separate from build data. Include edge cases and bad inputs.
3. Metrics and thresholds
- Accuracy / precision / recall per task (e.g. ≥ 95% on invoice field extraction)
- Groundedness: answer supported by sources (≥ 98% for finance)
- Refusal correctness: says 'I don't know' when data is missing
- Latency and cost per transaction
4. Test types
Functional, regression (prompt or model change), adversarial (prompt injection, jailbreak), bias and fairness where people are affected, security (data leakage), load.
5. Human review
Sample size and reviewer for each release.
6. Exit criteria
All thresholds met; no open critical defects; results signed off by business owner.
Read more
Oracle AI Agent Studio Demo: How to Plan and Run a PoC
Plan an Oracle AI Agent Studio demo or PoC for Fusion: pick a prebuilt or custom agent, prepare data and tools, add human approval, evaluate and run the script.
ArticleOracle AI Agent Studio Partner: How to Choose One
How to choose an Oracle AI Agent Studio partner for Fusion: first use case, human approval, testing, quarterly updates, scorecard, questions and red flags.
ArticleAI Project PMO Templates: Charter, Risks, Gates, Evaluation
PMO for AI projects: a practical set of templates, from AI project charter and use-case scorecard to risk register, stage gates, evaluation plan and hypercare.