A knowledge graph can boost the accuracy and explainability of LLM outputs. It provides the context needed to build an intelligent agent.

Graph data modeling takes a different mindset. You should focus only on relationships relevant to your specific use cases. Otherwise, you might overcomplicate your model, which will bog down queries and slow performance.

Fortunately, you can use The Model Context Protocol (MCP) to improve an AI’s reasoning abilities, so it can assist and make graph data modeling easier.

Join us on 20 November for a 30-minute overview of how this works. You’ll find out how to:

• Work with the open-source MCP Server for Neo4j
• Query context for AI-assisted graph data modeling
• Speed up development