Design Codebase Discovery

Value hypothesis

Helps designers better understand the current state of a live product's structure by giving them AI-mediated access to the codebase reality. The ability to look-through current architecture can inform design decisions, vocabulary, and tradeoff identification, reducing rework stemming from flawed assumptions about factors which may impact functional experience.

Insight · Velocity

The designer uses an AI coding assistant to query the production codebase before beginning work. When the codebase is treated as a research source rather than an implementation target, constraints and patterns can be revealed that are invisible in design tools: component properties, dependency relationships, legacy naming conventions, and behavioral states. The output informs design team before work begins.

Risks in application

Shallow Solutions

AI may return incomplete or misleading query results: offering less relevant information while missing factors that actually matter for design decisions.

Deskilling

Designers may reduce direct communication with engineers when AI-mediated codebase access provides sufficient implementation context, eroding the collaborative relationship that is critical for high-performing teams.

Expertise that differentiates

Technical Feasibility

Knowing what to ask - what sorts of calls, runtime behaviors, or data availability is most likely to impact key interactions or task flows.

AI Fluency that assures

Platform Awareness

Formulating queries that surface design-relevant information from code rather than implementation details that do not inform functional, interaction or visual design decisions.

Design Codebase Discovery

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Enables

Codebase-Extracted Design SystemIdentifies what needs extraction and documentation.Production Code GenerationScopes code generation to existing component patterns.