AI QA vs Traditional Software QA

Traditional QA assumes deterministic systems: the same input always produces the same output. AI QA works with probabilistic models, where multiple outputs can be acceptable and quality must be judged semantically, not by exact string match.

In practice, this means:

• Test cases become scenarios with evaluation criteria, not single expected strings.
• You track distributions, drift, and confidence rather than just pass/fail.
• Human evaluation and automated semantic metrics work together.

For a full overview of failure modes, test strategies, and checklists, see the AI Quality Assurance pillar page.