In our Retrieval Augmented Generation course, you’ll learn how LLMs generate tokens, why they sometimes “hallucinate,” and how grounding via retrieval improves factuality. You'll also explore tradeoffs like prompt length, compute costs, and context limits, using Together AI’s open-source models to run real examples and dig into the mechanics. 🔍 Learn how LLM internals shape RAG performance: https://hubs.la/Q03G2wlq0 | DeepLearning.AI

18K views · 352 reactions | A graph-powered all-in-one RAG system! RAG-Anything is a graph-driven, all-in-one multimodal document processing RAG system built on LightRAG. It supports all content modalities within a single integrated framework. 100% open-source. - https://github.com/HKUDS/RAG-Anything | Md Ismail Sojal | Facebook