manual_slop/MMA_Support/Overview.md

4-Tier Hierarchical Multi-Model Architecture (MMA) - Overview

The 4-Tier Hierarchical Multi-Model Architecture is a conceptual framework designed to manage complexity in AI systems by decomposing responsibilities into distinct, specialized layers. This modular approach enhances scalability, maintainability, and overall system performance.

Architectural Tiers

1. Tier 1: User-Facing Model (The Orchestrator/Router)

   • Direct user interface and intent interpretation.
   • Routes requests to appropriate specialized models or tools.

2. Tier 2: Specialized Models (The Experts/Tools)

   • Domain-specific models or tools (e.g., code generation, data analysis).
   • Performs the "heavy lifting" for specific tasks.

3. Tier 3: Data & Knowledge Base (The Information Layer)

   • A repository of structured and unstructured information.
   • Provides context and facts to specialized models.

4. Tier 4: Monitoring & Feedback (The Governance Layer)

   • Overarching layer for evaluation, error analysis, and continuous improvement.
   • Closes the loop between user experience and model refinement.

Core Goals

• Modularity: Decouple different functions to allow for independent development.
• Efficiency: Use smaller, specialized models for specific tasks instead of one monolithic model.
• Contextual Accuracy: Ensure specialized tools have access to relevant data.
• Continuous Improvement: Establish a systematic way to monitor performance and iterate.