A Product Designer’s AI Deck

MediumGetter Chen

AI summary focused on the core points of the original article.

Core Message

"Tools Come and Go, Output Stays"

Because AI tools in product design keep changing, the important ability is not locking a fixed set of tools into a permanent loadout. It is combining and replacing tools as cards for each situation, like a roguelike deck-building game.

Main AI Tool Combinations (The Current Deck)

MCP (Model Context Protocol): A support card that reduces the context switching caused by moving data between applications through direct integrations. Because it consumes many tokens, it is used selectively when needed.
LLM Wiki: A growth card that restructures past outputs and know-how so they can be reused continuously, with broader retrieval than RAG.
Codex / ChatGPT / NotebookLM: The main production cards that create actual outputs for design and knowledge work.
Skills & Figma Agent: The core combo cards used to receive objective design feedback and modify UI directly on the canvas.

Reasons for Swapping and Removing Tools (Swap When Appropriate)

A tool's usefulness is not permanent. Cards are benched when circumstances or performance change.

Antigravity: Removed after the 2.0 update caused computing resources to be consumed much faster.
n8n: Replaced with the local personal agent Hermes Agent because complex workflows were difficult to edit.
Nano Banana: Removed in favor of GPT-image and other options after its image-generation quality and aesthetic judgment declined.

Conclusion

Although the AI technology landscape and its meta continue to change, the experience and knowledge gained by combining tools accumulate in the LLM Wiki and become a foundation for the next task. The key is developing judgment about how to combine and evaluate tools—the experience of deck building—rather than becoming attached to the tools themselves.

XPLORED View

The strength of an AI tool stack comes not from the length of its list, but from the ability to combine and replace tools for each situation while preserving results and learning for the next task.

Good situations for reading

When you need criteria for deciding which rapidly changing AI tools to keep or replace
When connecting multiple tools has increased context-switching and maintenance costs

Points to take away

It is practical to judge whether a tool stays in the deck by output quality, context-switching cost, and resource consumption rather than by its name.
When outputs and working knowledge are accumulated separately from the tools, the foundation of the next workflow remains even after the tools change.

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