According to @AndrewYNg, a new short course titled Agentic Knowledge Graph Construction shows how a team of agents can extract and connect reference materials into a knowledge graph to build better RAG. Source: Andrew Ng on X, Aug 27, 2025, https://twitter.com/AndrewYNg/status/1960731961494004077 The course is taught by Neo4j Innovation Lead @akollegger, highlighting a practical graph-database approach for RAG pipelines. Source: Andrew Ng on X, Aug 27, 2025, https://twitter.com/AndrewYNg/status/1960731961494004077 Ng emphasizes that knowledge graphs are an important way to improve RAG quality. Source: Andrew Ng on X, Aug 27, 2025, https://twitter.com/AndrewYNg/status/1960731961494004077 For traders, the announcement contains no references to cryptocurrencies, tokens, or pricing, indicating no direct, immediate crypto-market catalyst from this post. Source: Andrew Ng on X, Aug 27, 2025, https://twitter.com/AndrewYNg/status/1960731961494004077

According to DeepLearning.AI, it launched a short course titled Agentic Knowledge Graph Construction in collaboration with Neo4j and taught by Andreas Kollegger to show how knowledge graphs complement RAG by modeling relationships and provenance for more reliable answers (source: DeepLearning.AI on X, Aug 27, 2025). For trading relevance, the announcement highlights enterprise demand for graph databases and agentic AI workflows in production QA systems, but it mentions no cryptocurrencies or digital assets, indicating no direct token-specific catalyst from this release (source: DeepLearning.AI on X, Aug 27, 2025).