AI Quality
AI systems need more than traditional test cases.
AI introduces probabilistic behavior, data dependency, prompt sensitivity, grounding issues, and evaluation challenges. Ekasala frames AI QA around behavior, risk, repeatability, and user trust.
Validation Targets
- Prompt and response quality
- Grounding and source behavior
- Hallucination and overconfidence risk
- Workflow impact and edge cases
- Regression across model or prompt changes
Operating Questions
- What does correct mean for this AI behavior?
- What output would harm the user or business?
- Where does deterministic testing still apply?
- Where do we need sampling, review, scoring, or guardrails?
- How do we explain readiness to leadership?