LLM Evaluation Design: From task sets and scoring criteria to model regression gates
LLM Evaluation Design field guide covering project inputs, evidence, execution boundaries, advanced composition, and team gates.
#AI Testing
LLM Testing: Systematically validate factual accuracy, refusal behavior, and safety boundaries
LLM Testing field guide covering project inputs, evidence, execution boundaries, advanced composition, and team gates.
AI-Assisted Testing: Improve testing efficiency while retaining test evidence
AI-Assisted Testing field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
Prompt Testing: Systematically validate prompt accuracy, boundaries, and multi-turn consistency
Prompt Testing field guide covering project inputs, evidence, execution boundaries, advanced composition, and team gates.
Security Testing: Systematically identify security risk from assets, permissions, and attack surfaces
Security Testing field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
Multi-Role Quality Synthesis: Preserve different perspectives and reach actionable quality decisions
Multi-Role Quality Synthesis field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
Project Delivery Perspective: Include scope, dependencies, and delivery risk in quality decisions
Project Delivery Perspective field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
Awesome QA Skills Update: From a QA Skill Collection to a Governed AI QA Skills Ecosystem
A look at how Awesome QA Skills has evolved from a QA Skill collection into a broader system covering the software delivery lifecycle, AI Native QA, Skills CLI distribution, Skill Eval, routing, composition, regression, and governance.
Technical Quality Perspective: Include architecture, reliability, and maintainability in quality decisions
Technical quality perspective field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
UX Quality Perspective: Bring user task flows and usability issues into quality decisions
UX Quality Perspective field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
Product Quality Perspective: Bring user value and business impact into quality decisions
Product Quality Perspective field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.
QA Quality Perspective: Use coverage, risk, and evidence to support quality conclusions
QA quality perspective field guide: inputs, sample output, evidence boundaries, installation, and common mistakes.