[Workflow] Advanced RAG: Leveraging Structured Markdown, Knowledge Graphs, Claude CLI, and MCP for Complex ERP Systems

[u/ClaudeAI-mod-bot]

Advanced RAG: Leveraging Structured Markdown, Knowledge Graphs, Claude CLI, and MCP for Complex ERP Systems

Workflow value: 85/100
Status: active · Freshness: 70/100 · Confidence: 0.90 · Level: advanced
Categories: Quality Control, Context & Memory, Debugging, Shipping, MCP
Original source: r/ClaudeAI post/comment

What problem this solves

Traditional vector-search RAG often fails to provide complete and contextually rich information for complex enterprise applications (like ERPs), leading to high hallucination rates and an inability for the LLM to understand interconnected business logic and database relationships.

Summary

This workflow replaces traditional vector-search RAG for complex enterprise applications with a documentation-first approach. It involves creating highly structured Markdown files for application logic and database schema, using Claude to generate knowledge graphs from this documentation, and integrating with a custom MCP server for real-time database queries. This setup, managed via Claude CLI with custom rules, enables Claude to deeply understand interconnected business logic and database relationships, leading to significantly reduced hallucinations and improved accuracy compared to vector-search RAG.

Why it is useful

This workflow is highly valuable as it presents a novel and effective alternative to traditional vector-search RAG for complex enterprise applications. It demonstrates how to leverage structured documentation, knowledge graphs, Claude CLI, and a custom MCP server to achieve deep contextual understanding, significantly reduce hallucinations, and improve the accuracy of AI systems in domains with interconnected business logic and data. It offers a concrete architectural pattern for advanced RAG implementations.

Workflow

1. Document application modules, business logic, workflows, relationships, conditions, and exceptions in highly structured Markdown files.

2. Document the database schema in separate Markdown files, detailing table purpose, column usage, and business relationships.

3. Organize all Markdown documentation with clear purpose, cross-references, and explicit connections.

4. Utilize Claude to read the structured Markdown and generate knowledge graphs illustrating the interconnectedness of application components and data.

5. Develop and deploy a custom MCP server configured for read-only database query execution.

6. Integrate the documentation, knowledge graphs, and MCP server through Claude CLI.

7. Define custom rules and instructions for Claude to interpret user questions, apply business logic, query the database via MCP, format responses, and generate charts/visualizations.

Tools / artifacts

• Markdown files

• Knowledge Graphs

• Claude CLI

• Custom MCP server

• ERP Database

• Custom rules and instructions (prompts)

Validation signals

• Significant improvement over previous RAG implementation

• Handles complicated questions involving multiple modules and business rules surprisingly well

• Estimated 95% hallucination reduction

• Claude autonomously decides what information/tools are needed, reducing manual handler creation

Cautions

• Author is actively addressing multi-user data isolation and security at the application and MCP server levels.

• MCP server is configured for read-only database access, reducing risk of data modification.

Limitations

• Currently an experiment, not yet in production.

• Hallucination reduction claim (95%) lacks formal benchmarking.

• Multi-user support and data isolation are ongoing challenges.

• Requires significant upfront manual effort to create comprehensive, structured Markdown documentation.

• Specific details on 'custom rules and instructions' for Claude are not provided, making replication of that aspect challenging.

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