Your alt text passes automated checks. That doesn’t mean it’s any good.

Many teams pass automated accessibility checks for image alternative text, yet still fail to deliver truly inclusive web experiences, a practical problem this content helps developers and operators address. The GitHub Engineering team has openly shared insights into their approach for evaluating alt text beyond mere syntax. Their work, rooted in an accessibility scanner plugin, demonstrates that while automated tools can catch basic errors like missing alt text, they often miss crucial nuances regarding context, relevance, and descriptive quality, which are paramount for users relying on screen readers. Their solution pushes beyond pass/fail to evaluate alt text for meaningfulness, helping teams understand *why* certain descriptions are insufficient and how to improve them. This directly affects anyone building or maintaining digital products, whether a web application, an internal tool, or a public-facing website. For an indie SaaS founder in Austin, Texas, developing an analytics dashboard, this means moving beyond simply having alt text on every chart to ensuring that the text accurately conveys data trends and insights to a visually impaired user, rather than just stating "bar chart of sales." A mid-sized e-commerce store in Portland, Oregon, selling artisanal goods can capitalize on this by training its content team to write alt text that truly describes product nuances, like "hand-thrown ceramic mug with speckle glaze and ergonomic handle," which enhances the shopping experience for all customers, potentially boosting conversions and reducing returns. Similarly, an internal IT team at a national healthcare provider headquartered in Chicago, Illinois, can apply this thinking to their electronic health records system, ensuring that images within patient portals or clinical dashboards provide rich, meaningful context for practitioners and patients using assistive technologies, improving data comprehension and reducing errors. To capitalize on this, teams should integrate human-centric review into their accessibility workflow for image descriptions. For instance, a freelance web developer in Denver, Colorado, currently relying solely on automated Lighthouse checks, could immediately start by picking a handful of key images on their current client's website. For each, they should try to describe the image's purpose and content *without* looking at the image, relying only on its alt text. If they can’t form a clear mental picture or understand the context, the alt text needs improvement. This small experiment reveals how current alt text truly functions for those who depend on it.