AI in QA: how to use Generative AI in testing without creating technical debt (www.techradar.com)

Generative AI is redefining QA from manual gatekeeping to a human-in-the-loop (HITL) oversight role: testers now supply context, constraints and review while models draft test cases, select suites and surface analytics. The payoff is speed—drafting scaffolds, cross-language documentation and repetitive steps—so junior testers learn faster and senior testers focus on exploratory and risk-based work. But unchecked one-shot generation often favors volume over precision, risking missed edge cases, misread business rules, bias, drift and hallucinations. Studies show widespread AI use in engineering (DORA: ~90% report some AI use) but persistent distrust (roughly one-third), and limited formal adoption in test pipelines (mapping studies report low explicit uptake), underscoring the gap between hype and safe practice. To avoid technical debt, teams must make review habitual and governance-first: require human approval before artifacts enter suites, provide rich context (systems, data, personas, negative/boundary cases), use templates for formats (step lists, BDD, free text), and log AI diffs plus acceptance vs rework. Prefer tools that explain rationale, link evidence (diffs, past failures, coverage gaps), emit confidence/risk scores, and preserve audit trails. Enforce role-based access, encryption, data-retention policies and prompt-training to reduce PII/exposure. In short, pair AI’s drafting speed with disciplined inputs, review checkpoints and traceability so accelerated testing compounds trust rather than hidden debt.