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 Governance Engineerrequired, working knowledge
- Ethical AI Specialistrequired, working knowledge
- AI Security Architectrequired, 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
12 graded topics, each passed by explaining it back. The first module of every program is free with a free account.
- Module 4: Practical AI Workflow Design and Prompt Engineering (1)
- Module 6: AI in the Workplace and Team Leadership (2)
- Module 8: Assessment and Continuous Learning (1)
- Module 13: Bonus: AI for Educators (1)
- Module 7: Technology, Platforms and Vendors (1)
- Module 9: Risk, Resilience and Frontier AI (1)
- Module 13: Measure, Sustain and Evolve (1)
- Module 6: Testing and Red-Teaming (2)
- Module 4: Evaluation and Trust (1)
- Module 23: Technical Credibility Deep Dive (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?
- 8 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?
- 12 graded topics teach it across 5 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.