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Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Where AI is failing design systems, and where we are failing AI
This content explores the challenges and opportunities of integrating AI into design systems, based on insights from 95 practitioners. It highlights the conflict between AI's probabilistic output and design systems' deterministic promises. The article suggests that AI excels at divergent tasks but struggles with convergent ones, advocating for treating AI as a collaborator rather than a fully autonomous factory. ✨
Article Points:
1
AI's probabilistic nature clashes with design systems' deterministic contracts.
2
Integrating AI shifts work from prompt engineering to complex context engineering.
3
AI is effective for divergent tasks (brainstorming, research) but fails at convergent ones (production code).
4
Design systems need explicit specs; AI can predict data but not guarantee output.
5
Treat AI as a collaborator for scaffolding and proposing, not a shipping factory.
6
Human-centric problems remain critical, even with advanced AI models.
Where AI is failing design systems, and where we are failing AI
Challenges
Probabilistic output
Erodes trust
Fails convergent tasks
Questionable code
Strengths
Excels divergent tasks
Brainstorming
Research assistance
Automates easy work
Strategies
Context engineering
Lean in where working
Experiment where not
Treat as collaborator
Human Factor