Advanced RAG Explained: From Vector Search to GraphRAG, Hybrid & Agentic AI Systems

Description

🌲 Advanced RAG: Evolution of Structural, Vectorless, and Hybrid Retrieval Retrieval-Augmented Generation (RAG) is rapidly evolving beyond traditional vector search. In this video, we explore how modern AI systems are moving toward reasoning-driven architectures that improve accuracy, auditability, and enterprise readiness. We break down the limitations of classic vector-based RAG, including context fragmentation and lack of logical understanding, and introduce advanced approaches such as GraphRAG, vectorless retrieval, and hybrid search systems. You’ll also learn how agentic workflows enable intelligent tool usage like Text-to-SQL, and how hierarchical indexing preserves document structure for better results in specialized domains. Additionally, we cover practical techniques to improve performance, including multi-layer caching, intelligent batching, and low-latency retrieval strategies. This video is ideal for AI engineers, architects, and teams building production-grade LLM systems using frameworks like vLLM. 📌 Topics Covered: * Limitations of traditional RAG * GraphRAG and structural retrieval * Vectorless and SQL-based retrieval * Hybrid RAG architectures * Agentic workflows * Performance optimization techniques 🚀 The future of AI is not just retrieval — it’s reasoning over structured knowledge. #AI #RAG #GraphRAG #LLM #GenerativeAI #RAG #AdvancedRAG #GraphRAG #VectorlessRAG #HybridRAG #LLM #GenerativeAI #AIArchitecture #AgenticAI #vLLM #MachineLearning #AIEngineering #TextToSQL #KnowledgeGraph #EnterpriseAI #AIInfrastructure