The 10-Stage LLM User Maturity Model
Stage 1 — Reactive User
The user immediately asks whatever comes to mind.
The focus is on receiving an answer.
The input is almost unchanged from raw language and has little structure.
Characteristics:
“What is this?”
“Explain this.”
“Summarize this.”
At this stage, the user treats the model similarly to a search engine.
Stage 2 — Request-Oriented User
The user begins to understand the desired output format.
They can request summaries, tables, comparisons, or examples.
However, they still do not directly influence the reasoning structure.
Characteristics:
“Summarize in a table”
“Compare pros and cons”
“Include examples”
This is where output format control begins.
Stage 3 — Constraint-Oriented User
The user can apply constraints such as length, style, scope, and restrictions.
They intuitively understand that poor output is often due to insufficient constraints.
Characteristics:
“Explain simply”
“Avoid technical terms”
“Only give three points”
“Separate assumptions from facts”
At this stage, the input becomes a constrained task rather than a simple question.
Note:
Early stages (1–3) often appear mixed in practice.
A single prompt may contain elements from multiple stages simultaneously.
Stage 4 — Structured User
The user decomposes the question into components.
They separately define goal, scope, constraints, and output format.
They understand that model performance is strongly influenced by input structure.
Characteristics:
Goal / Scope / Constraints / Output
Step-by-step requests
Define first, then compare
This is the practical starting point of prompt engineering.
Stage 5 — Verification-Oriented User
The user does not simply accept answers.
They request separation of facts, assumptions, and uncertainty.
They consider hallucination, inconsistency, and model limitations.
Characteristics:
“Separate evidence from assumptions”
“Only state what is certain”
“Say ‘unknown’ if unsure”
“Check for contradictions with previous answers”
At this stage, the user sees the model as a fallible reasoning system, not just an output generator.
Stage 6 — Framing User
The user applies a specific analytical framework.
They prioritize how the question is processed over what is being asked.
Characteristics:
“Focus on structure before meaning”
“Analyze by cause / effect / impact”
“Organize by pros / cons / risks”
“Break down into structure / flow / importance”
Core idea:
The user defines how to analyze, not just what to ask.
Stage 7 — Loop Design User
The user moves beyond single prompts and creates iterative structures.
They design loops of input → analysis → validation → refinement → feedback.
Output formats are treated as repeatable protocols.
Characteristics:
At this stage, the user treats the model not as a one-time tool,
but as part of a repeatable operational system.
Stage 8 — Meta Design User
The user moves beyond prompts and designs external structures.
They understand that the model is a black box and build meta-layers around it.
At this point, the user becomes closer to a system designer.
Characteristics:
Structural routing
External logging and validation flows
At this stage, the user is no longer just asking questions,
but orchestrating the system externally.
Stage 9 — Engine Orchestration User
Given the constraint that the model itself cannot be modified,
the user operates it as a system by generating, comparing, selecting, and refining multiple responses.
The focus shifts from a single answer to a structured process of candidate generation → evaluation → optimal selection.
Characteristics:
Generate multiple responses and compare them
Select and refine the most appropriate result
Emphasize consistency and reproducibility
At this stage, the model is treated not as a conversational partner,
but as a controllable reasoning engine.
This transition occurs when the goal shifts from producing better answers
to designing systems that consistently produce good outcomes.
Stage 10 — Interaction and Collaboration Design
This is the highest stage.
Here, the focus moves beyond individual prompts to designing how humans and AI collaborate.
It is not about generating answers, but about building continuously improving interaction systems.
Characteristics:
Define roles between the user and the AI
Design collaborative structures
Build iterative and improving workflows
Structure long-term problem-solving processes