Module 1 — The RAG Bottleneck & Evolution of Graph-Based Retrieval

▶ NEXT IN THE SERIES — Module 2 — Ontological Foundations & Graph Construction (LlamaIndex + Neo4j)    • Module 2 — Ontological Foundations & Graph...   ============================================================ Advanced GraphRAG & Knowledge Agents — a friendly, in-depth course on building structured, graph-based retrieval systems, from first principles to production multi-agent pipelines. MODULE 1 — The RAG Bottleneck & Evolution of Graph-Based Retrieval Why does standard, flat vector RAG break down on multi-hop questions, scattered documents, and "summarize the whole corpus" queries? In this opening module we unpack the core bottleneck of dense-vector retrieval and see how GraphRAG preserves both topological and semantic structure — turning a pile of text into a queryable network of entities, relations, and communities. In this module: • Why dense-vector RAG fragments meaning and truncates context • Multi-hop, non-contiguous, and global-summarization failure modes • How graphs preserve the relationships vector search throws away • The intuition behind GraphRAG and where this course is headed This is Module 1 of 8 in the "Advanced GraphRAG & Knowledge Agents" series — watch them in order via the playlist. Course roadmap: 1. The RAG Bottleneck & Evolution of Graph-Based Retrieval 2. Ontological Foundations & Graph Construction (LlamaIndex + Neo4j) 3. Hierarchical Community Indexing & Global-Local Search (Microsoft GraphRAG & DRIFT) 4. Dual-Level Retrieval & Incremental Updates (LightRAG) 5. Neurobiologically Inspired Long-Term Memory (HippoRAG) 6. Relation-Free Hierarchical Graph Architectures (LinearRAG) 7. Minimum Cost Subgraphs & Inference-Time Structuring (AGRAG & StructRAG) 8. Multi-Tiered Architectures, Workflows & Benchmarks (GraphRAG-Bench) #GraphRAG #RAG #KnowledgeGraphs #LLM #AI #VectorDatabase #RetrievalAugmentedGeneration #MachineLearning #Neo4j #AIEngineering