RAG is Dead. (Here is What Replaces It) скачать в хорошем качестве

RAG is Dead. (Here is What Replaces It)

Vector databases were a great start, but they aren't the future. While RAG (Retrieval-Augmented Generation) allows LLMs to "read" data, it doesn't help them "remember" or learn from it over time. Enter Agentic Memory. In this video, we explore the shift from simple semantic search to persistent, adaptive memory for AI Agents. We'll look at why static RAG is failing complex workflows and how frameworks like MemGPT and LangGraph are solving the "context window" problem forever. In this video, I cover: Why RAG is actually "dumb" (it retrieves, but doesn't understand). The definition of Agentic Memory (Short-term vs. Long-term). How Agents can update their own knowledge base autonomously. A look at the tech stack: MemGPT, Vector Stores, and Graph RAG. **🚀 Resources & Code RAG Is Dead, Long Live Agentic Memory 1 source Traditional Retrieval-Augmented Generation (RAG) changed how artificial intelligence accesses data, but its stateless and read-only nature is increasingly viewed as a limitation for modern applications. The provided text explores the shift toward agentic memory systems, which move beyond simple document fetching to allow AI to persist, update, and reconcile information over time. While standard RAG is excellent for finding static facts, it cannot learn from past interactions or maintain a continuous narrative across different sessions. Agentic retrieval introduces more intelligent routing, yet true agentic memory is required for AI to function as a long-lived, autonomous partner. This transition represents a move from basic information retrieval to a sophisticated read-write architecture that mimics human-like continuity. Ultimately, while RAG remains a vital component for grounding models, it is now part of a broader ecosystem focused on dynamic knowledge management.