AI evaluation and testing design
Technical Evaluation
What is AI evaluation and testing design?
Designs tests for factuality, robustness, fairness, safety and abuse resistance with rubrics, baselines and thresholds, and says what a score misses.
Where the frameworks place it: NIST AI RMF MEASURE 1 and 2; NIST AI 600-1.
The interview question it draws
Design the evaluation for a customer-service model before launch. What do you test, against what data, and what result blocks the release?
A strong answer walks through the practice itself, with one real case, what you decided, and what the evidence showed afterwards.
Roles that ask for it
- AI Evaluation Specialistcore, depth expected
- AI Model Validatorcore, depth expected
- AI Product Manager, responsible product editioncore, depth expected
- Model Risk Managercore, depth expected
- Chief Technology Officer, AI engineering editioncore, depth expected
- AI Engineer, production systems editionrequired, working knowledge
- AI Governance Engineerrequired, working knowledge
- Ethical AI Specialistrequired, working knowledge
- Agentic AI Engineer, orchestration editionrequired, working knowledge
- AI Security Architectrequired, working knowledge
- Robotics AI Engineer, embodied systems editionrequired, working knowledge
- Physical AI Safety Engineer, autonomous systems editionpreferred, working knowledge
Backgrounds that already carry it
- Model risk, credit risk and quantitative analysis (shown by a work product)
Test design and benchmark selection are the craft.
- A computer science or software engineering degree (described, not yet shown)
Testing is second nature; AI evaluation design is a new shape of it.
- A data science, statistics or analytics degree (described, not yet shown)
Evaluation design for a governance decision is a new frame for a known skill.
Where it is taught and graded
19 graded topics, each passed by explaining it back. The first module of every program is free with a free account.
- Module 3: Failure Analysis, Learning From How Robots Break (1)
- Module 6: Human Factors and Human-Robot Interaction (1)
- Module 7: Research, Benchmarking, and Staying at the Frontier (2)
- Module 10: Responsible Physical AI in Practice (2)
- Module 6: Testing and Red-Teaming (4)
- Module 11: The Capstone: Deploy and Defend (1)
- Module 4: Practical AI Workflow Design and Prompt Engineering (1)
- Module 6: AI in the Workplace and Team Leadership (1)
- Module 13: Bonus: AI for Educators (1)
- Module 4: Evaluation and Trust (1)
- Module 10: Evidence Engineering (1)
- Module 17: AI Governance, AI Risk, AI Security, and AI Assurance (1)
- Module 18: Governance, Risk and Compliance Technology, Automation, and Evidence Engineering (1)
- Module 9: Risk, Resilience and Frontier AI (1)
Questions
- What is AI evaluation and testing design?
- Designs tests for factuality, robustness, fairness, safety and abuse resistance with rubrics, baselines and thresholds, and says what a score misses.
- Which AI governance roles ask for AI evaluation and testing design?
- 12 roles on the map name it, and it is core to AI Evaluation Specialist, AI Model Validator, AI Product Manager, responsible product edition, Model Risk Manager, Chief Technology Officer, AI engineering edition.
- How do I learn and prove AI evaluation and testing design?
- 19 graded topics teach it across 6 programs. Each topic is graded by explaining it back against its own transcript, so a pass is evidence, not attendance. The first module of every program is free with a free account.
- What interview question tests AI evaluation and testing design?
- Design the evaluation for a customer-service model before launch. What do you test, against what data, and what result blocks the release? A strong answer shows the practice itself: Designs tests for factuality, robustness, fairness, safety and abuse resistance with rubrics, baselines and thresholds, and says what a score misses.