Chain of thought (CoT) in AI UX is a pattern that reveals intermediate reasoning, plans, or tool steps so users can audit how the answer was produced. It may appear as expandable Thought, Activity, or step traces (not always raw model tokens) and supports trust, debugging, and learning.
Essential for educational tools, problem-solving applications, and domains where showing step-by-step reasoning builds trust and enables learning.
| Product | Implementation |
|---|---|
| ChatGPT | Thought / Activity surfaces for deeper runs without crowding the main answer. |
| Claude | Extended thinking and tool traces can be inspected for agentic or hard tasks. |
| Gemini | Shows planning or research steps on complex queries alongside the answer. |
| Cursor / coding agents | Exposes plan and file/tool steps so developers can follow agent work. |
Copy this prompt to generate a production-ready implementation in Cursor, Claude Code, Lovable, or any AI coding agent.
Generate a production-ready implementation of the "Chain of Thought" AI interface design pattern.
Pattern Definition:Chain-of-thought UX means showing selected intermediate reasoning, plans, or tool steps so users can understand how the answer was produced, usually behind an expandable Thought or Activity control rather than as the only answer.
No. Show reasoning for complex, high-stakes, or agentic tasks where users need an audit trail. Keep it collapsed or off for simple answers to avoid cognitive and visual load.
Not always. Product CoT is a designed disclosure of plans, tools, or summaries. Model-internal tokens may differ and can be incomplete or stylized, so treat UI reasoning as an aid, not a legal proof.
Collapsible thinking and tool traces
Hover for source
Show how sure the model is about a claim
Concrete actions to validate uncertain output
Visualizing RAG
Attach verifiable sources to generated claims