中文 Best solution LM Teleg prompt
This report provides an expert-level analysis of integrating Telegram Bots with LM Studio. It addresses the architectural conflicts between large-scale Persona & Play datasets (30+ documents) and complex logic frameworks (Vault V5: 3 bots needed), providing a concrete execution blueprint for IT development teams.
I. Evaluation of Current Proposals
We have evaluated the three proposed options based on Token Efficiency, Logic Integrity, and User Experience (UX).
| Option | Description | Pros | Cons | Conclusion |
|---|---|---|---|---|
| Option A | Three Independent Bots | Maximum logic isolation; prevents instruction interference between stages. | Critical UX failure. Requires manual bot switching; context/history is lost; breaks immersion. | Not Recommended |
| Option B | Single Mega-Prompt | Lowest development overhead; everything fits in one System Prompt. | Token Explosion and "Logic Leakage." The model confuses behavioral guidelines across different stages. | Not Recommended |
| Option C | RAG-based Persona Retrieval | Highest token efficiency; handles massive documentation effectively. | Higher technical barrier; retrieval inaccuracies may cause "character drift" or personality instability. | Long-term Solution |
II. The Core Strategy: State-Aware Dynamic System Prompt Injection
To overcome the limitations of the LM Studio interface, we shift the responsibility of logic management to the Telegram Bot Backend (Middle-tier), which acts as the "Orchestrator."
Architectural Concept: Dynamic Injection
While the LM Studio API provides a single system_prompt field, it should not be treated as a static configuration. The backend code must dynamically synthesize the System Prompt based on the user's "Current State."
State 1 , State 2 , or State 3 .III. Technical Blueprint: The Middleware Controller Pattern
IT teams should implement the following pattern to ensure scalability and logical consistency.
1. System Architecture Flow