Uploaded July 2026 | Updated September 2026, 2 weeks ago
Are your GenAI applications struggling with complex, multi-hop reasoning and a lack of explainability? Discover why traditional vector-based RAG is breaking down in enterprise environments and how semantic knowledge graphs are the missing puzzle piece.
Cassie Shum breaks down the architectural limitations of traditional RAG when it comes to connecting the dots across massive organizational data. In this InfoQ presentation, you’ll learn how to build a GraphRAG pipeline that extracts entities from unstructured text and binds them with your core business logic. Cassie also cuts through the hype around AI agents, explaining why they are merely orchestrators - and why a semantic data foundation is the real key to decision intelligence.
⏱️ Video Timestamps (For Navigation)
0:00 - Intro: Cassie Shum & Scaling AI for the Enterprise
1:32 - The RAG Promise & Where Traditional RAG Breaks Down
4:28 - What is a Knowledge Graph? (Adding Semantics to Structure)
7:05 - The GraphRAG Pipeline: Extracting Entities from Unstructured Data
9:42 - Demo: Extending the "Jaffle Shop" Ontology
14:15 - The Truth About AI Agents (Memory vs. Correctness)
17:40 - The AI Architecture Stack: Semantic Layers & Reasoners
21:10 - Q&A: Data Locality, Storage Costs, & Architectural Decisions
🔗 Transcript & slides available on InfoQ: bit.ly/3RnAitc
#GraphRAG #SoftwareArchitecture #ArtificialIntelligence
Are your GenAI applications struggling with complex, multi-hop reasoning and a lack of explainability? Discover why traditional vector-based RAG is breaking down in enterprise environments and how semantic knowledge graphs are the missing puzzle piece.
Cassie Shum breaks down the architectural limitations of traditional RAG when it comes to connecting the dots across massive organizational data. In this InfoQ presentation, you’ll learn how to build a GraphRAG pipeline that extracts entities from unstructured text and binds them with your core business logic. Cassie also cuts through the hype around AI agents, explaining why they are merely orchestrators - and why a semantic data foundation is the real key to decision intelligence.
⏱️ Video Timestamps (For Navigation)
0:00 - Intro: Cassie Shum & Scaling AI for the Enterprise
1:32 - The RAG Promise & Where Traditional RAG Breaks Down
4:28 - What is a Knowledge Graph? (Adding Semantics to Structure)
7:05 - The GraphRAG Pipeline: Extracting Entities from Unstructured Data
9:42 - Demo: Extending the "Jaffle Shop" Ontology
14:15 - The Truth About AI Agents (Memory vs. Correctness)
17:40 - The AI Architecture Stack: Semantic Layers & Reasoners
21:10 - Q&A: Data Locality, Storage Costs, & Architectural Decisions
🔗 Transcript & slides available on InfoQ: bit.ly/3RnAitc
#GraphRAG #SoftwareArchitecture #ArtificialIntelligence
