Resource
Design-to-Code Workflow
See how design-to-code workflows evolve when GPT-Image-2 and ChatGPT Design reduce handoff friction between interface and implementation.
Design-to-code once meant manually translating static frames into front-end components. With ChatGPT Design and GPT-Image-2, the workflow shifts toward shared generation, live iteration, and system-level alignment where design and engineering move from the same source of truth.
The old handoff problem
Static mockups rarely capture responsive states, content rules, or interaction logic completely. Developers then rebuild intent from screenshots and annotations, which introduces inconsistency, delay, and expensive revision rounds.
The new workflow with GPT-Image-2
GPT-Image-2 helps teams generate visual directions that already encode hierarchy, tone, and structure. When that output is connected to ChatGPT Design, teams can move toward code-aware interfaces faster and review the same system from both design and engineering perspectives.
In this resource
- How modern design-to-code workflows differ
- Shared checkpoints for design and engineering
- Reducing translation loss in production teams