According to @DeepLearningAI, its Retrieval Augmented Generation course explains how LLMs generate tokens, why hallucinations occur, and how retrieval-based grounding improves factuality using Together AI’s tooling. According to @DeepLearningAI, the curriculum explicitly explores deployment tradeoffs including prompt length, compute costs, and context limits. According to @DeepLearningAI, this focus on cost and context constraints targets the practical variables practitioners balance when scaling LLM applications.

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).

According to Andrew Ng, a new Coursera course on Retrieval Augmented Generation (RAG) has been launched in collaboration with DeepLearning.AI. While the course focuses on building production-ready RAG systems, this development is significant for the cryptocurrency sector. Advanced AI capabilities like RAG are increasingly crucial for developing sophisticated crypto trading bots that can analyze vast amounts of market data, news, and social sentiment for more accurate predictions. Furthermore, this skill set is vital for building the next generation of decentralized AI (DeAI) applications on the blockchain, potentially driving innovation and value for AI-related crypto tokens.

According to Vitalik Buterin, a peculiar bug is causing his laptop to consume an excessive 25 watts when the ollama server is active, even if not in use. This has led him to implement keyboard shortcuts to efficiently toggle the server, which he finds inconvenient. Additionally, he is exploring lmstudio, noting its extensive features, particularly in handling large documents with Retrieval-Augmented Generation (RAG).