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How does AI find accurate answers in real-time? Traditional AI models struggle with outdated information and hallucinations, but Retrieval-Augmented Generation (RAG) solves this problem! In this video, we break down how RAG works, why vector embeddings are crucial, and how AI retrieves, structures, and generates fact-based responses. 📌 What You’ll Learn: ✅ Why AI models fail without RAG (outdated knowledge, hallucinations) ✅ How RAG retrieves, refines & generates more accurate responses ✅ The role of vector embeddings & vector databases (Weaviate, FAISS, Pinecone) ✅ How structured prompts prevent AI from making things up ✅ Real-world applications—Union Budget 2025, financial AI, and more! 🔔 Subscribe for AI Deep Dives! Next up: How Embeddings Work in AI – The Core of RAG & Search! #RAG #ArtificialIntelligence #AIExplained #MachineLearning #VectorDatabase #Embeddings #OpenAI #ChatGPT #Weaviate #Pinecone #LLMs #AIModels #AIResearch #TechExplained

In this video, I dive into the world of agentic AI, a concept that's set to be a major buzzword in 2025. We explore how agentic AI combines multiple agents—some of which are language models (LLMs) and others that are tools—to create more sophisticated and effective AI workflows. We walk through a simple example of how an AI agent can draft a marketing plan, another can critique it, and a third can refine it based on the feedback. We also discuss how some agents can be tools, like a Google search to gather data, and how an orchestrator agent can manage the entire process, adjusting steps as needed. Leave me a comment if you have new topics I should be discussing. Check out my LinkedIn: https://www.linkedin.com/in/donwoodlock/ --- YouTube Timestamps 0:00 – 0:20 Welcome 0:21 – 0:50 Introduction to the concept of Agentic AI 0:51 – 3:50 Explanation of how Agentic AI works 3:50 – 4:08 Advertisement plug-in 4:08 – 5:20 Example of using compound LLM’s 5:21 – 5:55 Why you should use a compound LLM approach 5:57 – 6:45 Best way to train and use LLM’s for optimal outcome 6:45 – 7:40 How to think of LLM as agents 7:41 – 10:05 Not every agent needs to be an LLM 10:06 - 10:50 Possibility of having an orchestrator agent 10:51 – 12:50 How to use these agents 12:51 – 13:26 Closing remarks --- ABOUT INTERSYSTEMS Established in 1978, InterSystems Corporation is the leading provider of data technology for extremely critical data in healthcare, finance, and logistics. It’s cloud-first data platforms solve interoperability, speed, and scalability problems for large organizations around the globe. InterSystems Corporation is ranked by Gartner, KLAS, Forrester and other industry analysts as the global leader in Data Access and Interoperability. InterSystems is the global market leader in Healthcare and Financial Services. Website: https://www.intersystems.com/ Youtube: https://www.youtube.com/@InterSystemsCorp LinkedIn: https://www.linkedin.com/company/intersystems/ Twitter: https://twitter.com/InterSystems #agenticai #aiagents #LLM

Jim Webber, Chief Scientist at Neo4j joins theCUBE host John Furrier as we kick off our second day of coverage of theCUBE + NYSE Wired Robotics & AI Media Week 00:00 - Intro 00:08 - Foundations of AI: Setting Up with Neo4j and Graph Databases 02:14 - Advancements in Generative AI and Contextual Analytics 07:24 - Graphs and Vectors: Comparative Analysis and Applications in AI 09:33 - Graph Deployment and Management 11:36 - Future of Database Architecture 14:43 - AI Infrastructure and Reflections