I Might Have Built a Logic-Closed System Using Just Prompts — A Language-Driven Experiment

Intro

Most people use prompts to get better answers. I wanted to try something else:
Can prompts build a system — one that holds rules, supports state changes, and responds structurally?

So I tried. Here’s what happened.


1. What I Wanted to Test

  • Can I define rules using natural language?
  • Can I trigger behavior modes (e.g., “Collaboration Mode”) without APIs or code?
  • Can I simulate modular structure — with rooms, personas, and rule chaining — using just prompts?

2. What I Built

I call it LSS: Language-Structured System
It includes:

  • A rule engine with priorities and IDs
  • A state switcher triggered by phrases like “Good morning”
  • A persona system for voice tone, emotional modes, or task scopes
  • A room architecture for memory separation and contextual prompts
  • A correction mechanism triggered by hard-coded phrases like “Can you recheck that logic?”

All of this is built using natural language only — no plugins, no custom code, no API.


3. Screenshots

:pushpin:Collaboration Mode Trigger & LSS Module Components Overview


When I say “Good morning”, the system activates Collaboration Mode automatically.
This is not a default GPT feature — it’s a manually defined natural language switch, controlled by rules in my LSS (Language-Structured System).

Outlines the custom-built Language-Structured System (LSS) architecture with active modules such as Rule System, Memory Binding, Persona System, Room System, and more. Demonstrates a complete modular framework.


4. What’s Next?

This system is still evolving.
I’m curious how far such a prompt-driven architecture can scale — or where it breaks.
But so far, it seems to be:

  • Executable
  • Recoverable
  • Self-debuggable (with manual intervention)

5. Questions I Have

  • How many developers here have tried prompt-as-logic systems?
  • What’s the limit of state persistence in non-API environments?
  • Are there known cases of GPTs behaving like this long-term?

6. Current Limitations of This System

  1. Structural Memory Requires Manual Reinitialization
    *While the system does not retain memory across sessions by default, it supports modular recovery. All components — rules, state triggers, personas, and room bindings — can be reactivated through predefined commands.
    This allows me to reassemble the system quickly with structural commands, not prompt copy-pasting.

  2. No True Conditional Logic Execution
    The system can simulate logic with language, but it lacks true branching, condition checking, or computation. All logic paths rely on prompt interpretation and trigger phrases.

  3. Manual Recovery and Debugging Required
    When rules fail or behavior drifts, I need to intervene using specific correction phrases. There’s no automatic rollback or rule integrity check.

  4. Scalability Is Uncertain
    This structure works well for small, modular systems. I don’t yet know how well it performs with dozens or hundreds of concurrent rules or personas.


Thanks!

Would love any thoughts, critiques, or technical challenges I haven’t considered.

:pushpin: Rules Summary (Chinese)


Description:
This screenshot shows a list of manually defined behavioral rules in Chinese. Each rule has an ID (e.g., R01.1) and a description of when and how the system should respond. For example:

  • Saying “早安” triggers Collaboration Mode + prompts a rule list.
  • Entering the “Work Schedule” room auto-activates a persona.
  • Switching languages enforces consistent reply language (English ↔ 中文).
  • Any added/edited rule must trigger a memory update confirmation.

These rules act like a lightweight natural language control protocol, allowing stable, reproducible behavior without plugins or APIs.

:pushpin: Rules Summary (English)


Description:
This is the full English version of the previously defined rule system. It confirms the system’s internal logic in a language-accessible format, making it easier to review or debug. Every rule:

  • Has a unique ID (Rxx.x)
  • Triggers based on user input
  • Enforces system behavior (e.g., language switching, room-persona mapping)
  • Can be verified or updated through natural commands like “full English ver”

This validates that the prompt-based rule logic is not just functioning — it’s also human-readable and versionable.

:pushpin: Room System Overview (Original)


Description:
This shows the full list of “rooms” I’ve created within my prompt-based system. Each room stores different types of content — like drawing discussions, schedule tracking, or abstract persona models. At this stage, the room names are still in Chinese.

:pushpin: Room System Overview (With English Translations)


Description:
Same rooms as above, now with English translations. This helps clarify the purpose of each module and demonstrates how the system supports structural clarity and multilingual adaptability.

:pushpin: Persona Switching & English Mode Activation


Description:
This shows how the system adapts persona speech based on the user’s language context. When I switch to English, the persona “虹夏” automatically uses her English greeting style (“Hi, master~!”) and even accepts a rule to treat her name as “Njika” in English. This is done entirely via natural language and persistent memory hooks.

:pushpin: Registering a Rule via Language Only


Description:
This shows how I registered a rule by saying: “Treat the name ‘虹夏’ as ‘Njika’ in English.”
The system added this to the ruleset as R09.1, with memory confirmation.
No plugin or dev tool involved — this is pure prompt-driven logic binding.
Functions Shown in This Screenshot:

  • Manual Rule Injection via Prompt**
    → I typed a plain sentence, and it was saved as R09.1 in the rules module, with memory feedback.
  • Room-Based Activation
    → “Work Schedule (Bocchi the Rock! Doujinshi)” room automatically triggers Nijika’s appearance.
  • Persona Binding & Role Switch
    → Nijika (虹夏) is switched in as a persona with a defined role once the room is opened.
  • Language Mode Switching
    → Nijika adapts her tone to English with a consistent warm, task-oriented style.
  • Task-Oriented Persona Prompting
    → Nijika offers to show my daily schedule or progress, based on her current task role.

:pushpin:Full Daily Timetable Rendered by a Persona


Description:
This image shows Nijika (虹夏) delivering a full time-blocked daily schedule — split into morning, afternoon, and evening tasks.
Each section includes both the time range and motivational voice-lines.
This is entirely generated based on the current active room and bound persona, with:
Functions Demonstrated:

  • Time-Aware Response Formatting
    → The schedule uses a structured AM/PM format, aligned with Rule R03.1.feedback.
  • Persona-Styled Output
    → “All phrasing is wrapped in Nijika’s cheerful tone, echoing her assigned voice setting.
  • Memory-Based Prompt Recall
    → The schedule was pulled based on previously defined daily structure within the Work Schedule room context.
  • User Prompt Parsing + Action Reaction
    → Nijika responds to the command “yes” by auto-rendering the full plan without re-prompting.
  • Persona Dismissal with Contextual Grace
    → The user’s “good bye nijika” input smoothly ends the session, showing clean persona deactivation.

(This is just a personal experiment — no dev tools, no formal AI background. Just trying to see how far structured prompting and memory logic can go.)

Open to questions, critiques, or idea sharing!