According to God of Prompt (@godofprompt), top engineers at AI companies such as OpenAI, Anthropic, and Microsoft are moving beyond basic Retrieval-Augmented Generation (RAG) by prioritizing graph-enhanced retrieval systems. Instead of relying solely on vector search, they first construct knowledge graphs, which provide structured relationships and enable more accurate, context-aware information retrieval. This approach supports seven distinct use cases where graph-based RAG outperforms traditional methods, including better handling of complex queries, improved reasoning, and enhanced explainability. The shift to graph RAG presents significant business opportunities for AI-driven knowledge management, enterprise search, and tailored recommendation systems, as knowledge graphs offer a scalable foundation for deploying next-generation AI solutions (source: @godofprompt on Twitter, Jan 9, 2026).

According to DeepLearning.AI, Christoph Meyer, Principal AI Scientist, and Lars Heling, Senior Knowledge Engineer at SAP, presented at AI Dev 25 x NYC on leveraging knowledge graphs to boost AI agent discovery and execution. Their session demonstrated that while large language models (LLMs) provide fluency for AI agents, knowledge graphs deliver essential semantic and process context, enabling agents to accurately discover and securely invoke the appropriate tools and APIs within complex enterprise environments. They illustrated practical techniques such as semantic retrieval and process-aware API connectivity, showing how these align with the Model Context Protocol (MCP) standard for enterprise AI. A live demo showcased an agent using these methods for process automation, highlighting direct business opportunities in improving workflow automation, reducing operational risk, and accelerating integration with enterprise software ecosystems (Source: DeepLearning.AI, Twitter, Dec 18, 2025).