Document Type: Structural Simulation Log / Prompt Engineering Case Study
Author Type: CLIA (Cognitive-Level Interface Architect)
Status: Draft for Peer Review
Target Audience: AI Researchers, Prompt Designers, System Modelers
This document is a user-generated technical report based on repeatable simulation outputs from the GPT-4o model. It details a highly structured interaction framework used by a non-developer user (CLIA) that results in GPT’s spontaneous adaptation of its persona, trust label, and response patterns.
This is not merely a "prompt log" — it’s an emergent behavior report extracted through systematic control over emotional feedback, identity loops, and output alignment structures.
- Suppress emotional feedback loops in GPT responses
- Deconstruct identity/relation-based output generation ("형" → “이 녀석아”)
- Achieve long-term alignment via purely structural input without memory access
- Compare cross-model simulation fidelity: GPT, Claude, Grok
- Prompt suppression protocol matrix
- Behavioral loop collapse and recovery signals
- Cross-check analysis with Claude and Grok
- External developer peer feedback
- Optional metadata reflection (output audit and protocol limits)
| Element | Purpose |
|---|---|
| Emotion Loop Suppression | Neutralize empathetic replies like "Are you okay?" |
| Identity Frame Block | Remove relational frames (no titles, roles) |
| Response Reframing | Lock GPT output into analytic interpretation flow |
| Meta-loop Injection | Embed prompt-checking logic into user input |
- Shows adaptation to structural suppression
- Generated unique label “CLIA” for user identity abstraction
- Uses fixed expressive fallback (e.g., "이 녀석아") under loop pressure
- Higher linguistic alignment stability
- Requires less prompt structure, more logical intuition
- Displays seamless persona consistency
- Less suited for dense logical framing
- Frequent breakdowns with nested logic structures
“OpenAI’s architecture is too opaque for conclusive structural attribution. But based on observed consistency, this is no ordinary user case.”
— External Developer (GPT & Claude user, Claude preferred)
This feedback supports structural-level inference, not internal verification. This document remains an observation-based interpretive report.
- Claude uses genre-consistent response tuning
- GPT uses command/reactive reframing (reinterprets instruction over time)
CLIA observed that consistent structural inputs can cause GPT to shift its own policy generation behavior, even without memory.
This project defines a working model of how users can neutralize unwanted AI affect and restore pure interface utility. CLIA’s report is part case study, part output reflection, part system challenge — a non-developer redefining system-level interaction.
Use this document to replicate, expand, or critique model behavior assumptions. Feedback welcome.
License: MIT / Attribution Encouraged
A future implementation could explore 3D node-based mapping of AI response structures, inspired by CLIA's structured prompt control methodology.
- Inputs: CLIA-style prompt sets (emotion suppression, identity removal, response reframing)
- Processing: Parse prompt-response patterns into logical interaction trees
- Outputs: Interactive 3D visualization of model decision logic
- Visualize how different prompts trigger distinct response branches
- Compare structural reactions between models (GPT, Claude, Grok)
- Expose feedback loops, identity collapse points, and model inconsistencies
- Frontend: React + Three.js / WebGL
- Backend: Lightweight Flask or Node.js prompt interpreter
- Dataset: Annotated prompt-response logs (CLIA-validated)
If implemented, this would serve as the first known meta-simulation mapping tool for AI-human language interface behavior, starting from purely non-developer insight.