Skill guide
AI and LLM testing
AI testing evaluates probabilistic outputs for qualities such as faithfulness, relevance, safety, robustness, fairness, and task success.
Why this capability matters
Traditional pass/fail tests are not enough when outputs vary, models change, and quality depends on context.
What competent practice includes
Golden-dataset design
Task-specific evaluation criteria
RAG and prompt regression testing
Adversarial cases
Quality gates in delivery pipelines
Portfolio evidence
An evaluation harness with documented cases, scoring logic, baseline results, regression thresholds, and a findings report.
See public project briefsProfessional applications
These are fields of application, not guaranteed job or income outcomes.
RAG quality assurancePrompt regressionAgent testingModel comparisonAI release readiness
Courses that develop this skill
AI Security
AI LLM Testing
Quality engineering for AI systems — testing non-deterministic LLM and RAG outputs the way QA tests deterministic code.
AI Security
AI Security
Defending LLM applications and agents against prompt injection, jailbreaks, and the OWASP Top 10 for LLMs.
AI Operations
LLMOps
Operationalizing large language models specifically — prompt versioning, evaluation, cost, and deployment at scale.
Connect the skill to a market path
Learn how this capability fits inside a complete problem, proof, service, and delivery journey.