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
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
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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.
-
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.
-
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.
-
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.
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.
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.
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.
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.
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.
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.
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!
