{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreieejr4zgbnl4zwqdxhgpf5j4yalrgfhxbjoa5l66qrx6txxo2vbam",
    "uri": "at://did:plc:lk3jfj3zq4k4wxnk474axylu/app.bsky.feed.post/3mk2layqrybr2"
  },
  "path": "/t/from-prompting-to-system-design-a-10-stage-model-of-llm-users/1379453#post_3",
  "publishedAt": "2026-04-22T02:12:36.000Z",
  "site": "https://community.openai.com",
  "textContent": "How Do Users Evolve in Prompting?\n\n-– The 10-Stage LLM User Maturity Model\n\n  1. Problem Statement: Is It Skill or Evolution?\n\n\n\nMany people use LLMs (Large Language Models), yet the quality of results varies significantly.\nThis difference is often attributed to “communication skill,” but that is not the core issue.\n\nWe should instead ask:\n\n“How do users evolve in their ability to operate an intelligent reasoning engine?”\n\n  2. Limitations of Current Approaches: Tips Without a Model\n\n\n\nMost prompt-sharing practices today are fragmented:\n\nPrompting tips\nCopyable templates\nModel comparisons (benchmarks)\n\nWhat is missing is a discussion of:\n\nA structured model of user progression\n\n  3. Proposal: A 10-Stage Model of Prompt User Development\n\n\n\nThis model is not based on knowledge level,\nbut on the ability to structurally control input.\n\nPhase 1: Message Control Stage\n\nStage 1 (Reactive)\nThe user inputs whatever comes to mind and treats the model like a search engine.\nExample: “What is this?”\n\nStage 2 (Request-Oriented)\nThe user specifies the output format.\nExample: “Summarize this in a table”\n\nStage 3 (Constraint-Oriented)\nThe user adds constraints and understands that output quality depends on them.\nExample: “Explain in simple terms, in three points”\n\nStage 4 (Structured)\nThe user breaks the question into components such as\nGoal / Scope / Constraints / Output.\n\nStage 5 (Verification-Oriented)\nThe user questions the output and asks for separation of\nfacts, assumptions, and uncertainty.\n\nNote:\nEarly stages (1–3) often overlap in practice, and a single prompt may include multiple elements simultaneously.\n\nPhase 2: Reasoning System Stage\n\nStage 6 (Framing)\nThe user applies a structured analytical framework to guide reasoning.\n\nExamples:\n\n“Analyze in terms of cause / effect / impact”\n“Organize by pros / cons / risks”\n“Break down into structure / flow / importance”\n\nCore idea:\nThe user defines how to analyze, not just what to ask.\n\nStage 7 (Iterative Loop)\nThe user moves beyond one-off prompts and designs a process:\nInput → Processing → Validation → Feedback → Iteration\n\nStage 8 (Meta Design)\nThe user designs external layers that coordinate the model’s reasoning process,\ntreating the model as a black box.\n\nPhase 3: Engine Operation and Governance Stage\n\nStage 9 (Engine Orchestration)\nThe user treats the model as a reasoning engine.\nThey generate multiple responses, compare them, and select the best outcome.\n\nThe process becomes:\nCandidate generation → Evaluation → Optimal selection\n\nNote:\nThis transition occurs when the goal shifts from producing better answers\nto designing systems that consistently produce good outcomes.\n\nStage 10 (Interaction and Collaboration Design)\nThe user designs how humans and AI collaborate.\nThe focus shifts from generating answers to building structured, continuously improving interaction systems.\n\n  4. Key Insight: Not a Level, but a State\n\n\n\nThe most important insight of this model is that users are not fixed at a specific stage.\n\nSimple questions → Stage 2\nComplex design tasks → Stage 9\n\nIn other words:\n\nUsers do not stay at a level — they transition between states\n\nCapability Measure\n\nAn expert is not someone who always operates at Stage 10,\nbut someone who can elevate the system to Stage 9–10 when needed.\n\n  5. Example: Evolution of a Diet Question\n\n\n\nStage 1\n“Tell me how to diet”\n→ Simple information listing\n\nStage 5\n“Separate scientific evidence and uncertainty, and include falsifiable research findings”\n→ Verified information\n\nStage 9\n“Generate multiple diet strategies (e.g., low-carb, intermittent fasting),\ncompare their success likelihood based on my physical condition,\nand propose the optimal approach”\n→ Structured optimization\n\n  6. Conclusion: Those Who Control Structure Control Intelligence\n\n\n\nPrompting is not just a communication skill.\n\nIt is a reflection of the user’s ability to design systems.\n\nThe key is not what you know,\nbut how you structure and guide reasoning.\n\nFinal Summary\n\nUsers do not evolve toward asking better questions.\nThey evolve toward becoming system designers who control structure.",
  "title": "From Prompting to System Design: A 10-Stage Model of LLM Users"
}