According to @deeplearningai and @akollegger, their new course 'Agentic Knowledge Graph Construction' demonstrates how leveraging a team of AI agents can automate the extraction and connection of reference materials into a unified knowledge graph for Retrieval-Augmented Generation (RAG) applications (source: deeplearning.ai/short-course). The course, taught by Neo4j Innovation Lead Andreas Kollegger, focuses on practical skills such as building, storing, and accessing knowledge graphs using the Neo4j graph database, and implementing multi-agent systems with Google’s Agent Development Kit (ADK). By automating tasks like entity extraction, relationship mapping, deduplication, and fact-checking, agentic workflows significantly reduce manual labor and increase retrieval accuracy. This approach enables businesses to trace issues, such as customer complaints, directly to suppliers or manufacturing processes, turning unstructured data like invoices and product reviews into actionable business intelligence. The course highlights how knowledge graphs provide more precise information retrieval than vector search alone, especially in high-stakes scenarios where accuracy is critical (source: deeplearning.ai/short-course).

According to DeepLearning.AI (@DeepLearningAI), a new short course titled 'Agentic Knowledge Graph Construction' has been launched in collaboration with Neo4j and led by Andreas Kollegger (@akollegger). The course focuses on practical integration of Retrieval-Augmented Generation (RAG) with knowledge graph technology, highlighting how RAG retrieves relevant text data, while knowledge graphs provide structured modeling of relationships and provenance. This combination enables more accurate and explainable AI-powered answers, offering tangible benefits for enterprises seeking scalable knowledge management and advanced search solutions (Source: DeepLearning.AI, Twitter, August 27, 2025).