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  "path": "/t/feature-request-hierarchical-collapsible-outline-mode-for-long-chatgpt-responses/1382154#post_1",
  "publishedAt": "2026-05-31T18:54:20.000Z",
  "site": "https://community.openai.com",
  "textContent": "## Feature Request: Hierarchical / Collapsible Outline Mode for Long ChatGPT Responses\n\n* * *\n\n## 1. Problem\n\nChatGPT responses often contain valuable information but are presented as long linear text.\n\nThis creates usability issues:\n\n  * Users must scroll and scan sequentially to locate relevant parts\n\n  * Key conclusions are often embedded within dense explanations\n\n  * Users frequently request follow-up summaries or simplifications\n\n\n\n\nAs a result, information is available, but not efficiently navigable.\n\n* * *\n\n## 2. Proposed Solution\n\nIntroduce a **Hierarchical Outline Mode** for long-form responses.\n\nInstead of a single continuous text block, responses are rendered as a **collapsible structured outline** :\n\n  * A short **top-level summary is always visible**\n\n  * Major sections are shown as **expandable headings**\n\n  * Subsections can be expanded progressively on demand\n\n\n\n\nThis enables “skim → drill-down” interaction without re-prompting.\n\n* * *\n\n## 3. Implementation Note\n\nNative support would allow the model to explicitly generate hierarchical structure (section boundaries and abstraction levels) rather than relying on post-hoc UI inference from flat text.\n\nThis enables more accurate and consistent decomposition of responses into meaningful sections, improving both rendering quality and navigability.\n\nImportantly, this feature can still be implemented as a **presentation layer over a single model response** , without requiring multiple model calls during expansion.\n\n* * *\n\n## 4. Interaction Model\n\n  * Default view:\n\n    * Summary visible\n\n    * Section headings visible (collapsed)\n\n  * User interaction:\n\n    * Click/tap to expand sections\n\n    * Expand only the depth needed\n\n    * No re-prompting required for navigation\n\n\n\n\nExample structure:\n\n  * Summary\n\n  * Core Explanation\n\n  * Supporting Arguments\n\n  * Examples\n\n  * Edge Cases / Limitations\n\n\n\n\n* * *\n\n## 5. Benefits\n\n### 5.1 Reduced cognitive load\n\nUsers no longer need to parse long linear text to extract structure.\n\n### 5.2 Faster information retrieval\n\nRelevant sections can be accessed directly via structure rather than scrolling.\n\n### 5.3 Better support for complex responses\n\nEspecially beneficial for:\n\n  * technical explanations\n\n  * debugging discussions\n\n  * research summaries\n\n  * multi-step reasoning tasks\n\n\n\n\n### 5.4 Fewer follow-up simplification requests\n\nReduces need for:\n\n  * “summarize this”\n\n  * “give key points”\n\n  * “explain more simply”\n\n\n\n\n### 5.5 Aligns UI with natural human reading behavior\n\nUsers naturally navigate structured information via headings and hierarchy rather than continuous text.\n\n* * *\n\n## 6. Key Design Insight\n\nLLM outputs already contain implicit structure (summary, reasoning, examples, constraints).\n\nToday, this structure is flattened into linear text.\n\nThis proposal makes that structure **explicit and navigable** , without changing the underlying model output format or requiring multiple inference calls.\n\n* * *\n\n## 7. Expected Outcome\n\nChatGPT responses become:\n\n> navigable, hierarchical knowledge documents instead of linear chat streams",
  "title": "Feature Request: Hierarchical / Collapsible Outline Mode for Long ChatGPT Responses"
}