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Visual QA
Primer · 8 min read
A repeatable visual validation process produces consistent results regardless of who runs it or when. Building one requires a shared design reference, a systematic comparison method, and clear criteria for what requires a fix.
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Design-to-code
Why AI-generated frontends still need visual validation
AI-generated frontends still need visual validation because generating code from a design and verifying that the rendered output reflects the design are two different things.
Design handoff
What designers can do when implementations don't match
When a developer's implementation does not reflect the design, the designer is typically the one who notices first. What they do next determines whether the issue gets fixed efficiently or becomes a prolonged back-and-forth.
How to reduce design-to-code feedback loops
A design-to-code feedback loop is the cycle that happens when a developer implements something, a reviewer finds differences, and the developer goes back to fix them. Most of the length in these loops is avoidable.
How to compare your Figma design against localhost
You can compare your Figma design against localhost using a browser extension. Unlike web-based tools, it runs inside the browser and can reach local pages without deployment or tunnelling.
Design drift in AI-assisted development
Design drift is what happens when a frontend implementation diverges from its original design over time. AI-assisted development does not eliminate drift, but it changes where drift comes from and how quickly it accumulates.
The new frontend workflow: design, generate, verify
The frontend workflow that makes the most of AI generation has three stages: design in Figma, generate an implementation, then verify the rendered output against the design before shipping.
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