Concept·Workflow Infrastructure·Agency·Emerging·CON-024
Design Skills, Contracts and Evals Development
Value hypothesis
Builds organisational AI capability through reusable files that reduce variance in AI output quality across team members and compress the time practitioners spend learning to prompt effectively. Inc.
Learning · Efficiency
The design team builds, uses and maintains organisational AI infrastructure to help codify execution and review quality of repeatable activities. These instruction files, system prompts, work contracts, and design evals help standardise how team members use AI tools. "Prompt ops" encourages shared practice and facilitates onboarding. Quality management goes from reactive to preventive: teams integrate compliance, heuristics, and functional baselines into the generation context before work begins, rather than correcting AI output afterwards.
Risks in application
Pseudoproductivity
Instruction files may appear to standardise practice while encoding shallow or misaligned guidance; teams may believe they have quality governance despite templates being too generic to materially improve output quality. "Eval theater" aligns quality on fulfilling automated tests rather than demonstrable value.
Deskilling
Instruction files created without documented reasoning become organisational dependencies no one can explain or adapt when AI tools change; the rationale for specific constraints is lost before anyone thinks to record it.
Expertise that differentiates
Design System Logic
Structuring instruction files with the same architectural discipline as design system components: composable, reusable, maintainable, and consistent across different team members' workflows.
Business Framing
Defining which organisational constraints, quality standards, and brand requirements need to be encoded, and prioritising which workflows benefit most from standardisation.
AI Fluency that assures
Performance Discernment
Humans must the supply organisational insight necessary to define constraints, standards, and requirements, and to prioritize which workflows benefit most from standardisation.
Outputs must be monitored to verify that desired quality improvements are delivered, not just that the templates are well-structured.
Related
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Enables
Possible Indicators
Knowledge asset creation
Growth of template libraries; rate of revision and maintenance.
Practice diffusion
Rate at which AI-enabled practices spread across the team; reuse of prompt and skill templates.
Sources
Pendharkar, S. (2026). Conducting Pre-Research with AI Agent Personas: Pressure-testing concepts for expert workflows [conference talk]. Designing with AI 2026, Rosenfeld.
Van Dijck, P. (2026). Building New AI Skills (evals): creating outsized UX value [conference talk]. Designing with AI 2026, Rosenfeld.
Teeters, R. (2026). Concept to code: Transforming ideas into functional products with Kiro [conference talk]. Designing with AI 2026, Rosenfeld.
Lowson, A. (2026). Rehashing the Double Diamond: collaborating across functions with AI-assisted prototyping [conference talk]. Designing with AI 2026, Rosenfeld.
Chapelle, B. (2026). Leading through ambiguity: Supporting a design team relearning their craft [conference talk]. Designing with AI 2026, Rosenfeld.
Oduye, A. (mod.), Crumlish, C., Flowers, E., et al. (2026). From tools to staff: What the next generation of agents means for the future of design [panel]. Designing with AI 2026, Rosenfeld.