ChatGPT itself offers great optimisations to initial research and document writing for industry practitioners. However, it also suffers from key issues, such as not being able to reference the sources of its ideas. It also has the potential for making incorrect inferences that could be mistakenly accepted as factual by end-users, used in workplace documentation, or in the worst case, published on the internet, and then utilised in future releases of AI-based tools, creating negative feedback loops in human learning. In this talk, we will look at the current benefits and shortcomings of AI-based software quality improvement tools, and the challenges of testing AI-based software from a Quality Engineer’s perspective. We will look at international standards and training courses for AI development and testing. We will also explore problems that quality engineering practitioners would like to see solved in the future, to enable a wholistic approach to AI-augmented software quality engineering.