Generative AI (GenAI) is transforming software quality assurance (QA) by shifting responsibilities from manual oversight to real-time management of machine-generated outputs. While 90% of tech professionals are leveraging AI, many express skepticism about its accuracy. GenAI tools often produce tests with high output but low precision, necessitating substantial cleanup. This evolving landscape mandates QA teams to refine AI-generated content rather than create from scratch. Human-in-the-loop (HITL) processes are essential, involving contextual prompts and review checkpoints to mitigate risks such as bias, errors, and compliance failures. Effective collaboration between AI and human testers can enhance quality and efficiency, allowing teams to focus on high-level analysis and exploratory testing. By implementing structured guidelines and strong governance, organizations can build trust in AI outputs and ensure alignment with business requirements. The successful integration of GenAI hinges on clear communication, context-driven prompts, and robust review processes that uphold software quality.
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