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Artificial intelligence has aggressively entered the discourse of visual testing tools: "Visual AI", "self-healing tests", "smart diff" — every vendor claims its own AI layer. Behind the marketing terms, the technical reality is more nuanced — most often it boils down to perceptual algorithms (SSIM, embeddings) trained to filter cosmetic noise so that only likely meaningful differences surface. That is useful, sometimes remarkable, but it is not magic: a false negative in a visual test is still a bug shipped to production.

This page gathers articles that examine AI in testing with a critical eye: what the Visual AI promises of major vendors (Applitools first and foremost) actually cover, where the contribution is tangible and where it leans more toward marketing, how to evaluate the reliability of a so-called intelligent algorithm without falling into the black-box trap, what precautions to take when a tool decides on its own whether a difference is "important". We also cover the rising topic of LLMs applied to testing — scenario generation, automatic diff classification, review assistance — distinguishing promising uses from empty announcements. Delta-QA does not use generative AI in its current comparison engine, and these articles take a deliberate stance of honesty about what AI really brings to visual testing today.