For the CLD6000 Legal Text Analysis project, we need a highly coordinated approach across multiple AI systems. Here's my analysis:

1. Strengths of the Proposed System:

• Clear role separation between AIs
• Structured documentation flow
• Integration with assessment requirements

1. Potential Improvements Needed:

# Current Challenge:
LegalTextAnalysis/
├── Multiple AI inputs
│   ├── Code analysis (Claude 1)
│   ├── Documentation (Claude 2)
│   └── Architecture (GPT)
└── Synchronization points needed

# Proposed Enhancement:
class AIWorkflowManager:
    """Centralized workflow management"""
    def __init__(self):
        self.notion_base = "Legal Text Analysis Project"
        self.obsidian_vault = "CLD6000 Documentation"

    def coordinate_ai_responses(self):
        """Synchronize AI inputs"""
        # MCP database as source of truth
        # Notion for prompt management
        # Obsidian for technical documentation

1. Practical Implementation:

For our specific project, I recommend:

a) Primary Workflow:

graph TD
    A[Claude 1: NLP Pipeline Code] -->|Code Review| B[GPT Pro: Architecture]
    B -->|Implementation| C[Claude 2: Documentation]
    C -->|Assessment| D[GPT Enterprise: Integration]
    D -->|MCP Database| A

b) Documentation Structure:

Obsidian/CLD6000/
├── NLP_Pipeline/
│   ├── feature_engineering.md
│   └── text_processing.md
├── Assessment/
│   ├── weekly_progress.md
│   └── final_report.md
└── Integration/
    ├── mcp_setup.md
    └── database_schema.md

Would you like me to:

1. Detail the specific prompts we should use for our legal text analysis?
2. Create a template for coordinating between AIs on specific tasks?
3. Show how to integrate this with our existing MCP database structure?

This approach ensures we maintain clear documentation while leveraging multiple AI systems effectively.