LangGraph + Neo4j Crash Course for Beginners: AI Agent Development using Knowldege Graph | Hands-on! - GraphRAG vs TemporalRAG: Which One Should You Actually Use? (Full Breakdown)

🔗 RESOURCES - TG-RAG Paper: https://arxiv.org/abs/2510.13590 - GraphRAG Paper: https://arxiv.org/abs/2404.16130 Here's the problem nobody talks about: Your RAG system can't tell the difference between 2015 and 2023. Ask "Who was Apple's CEO?" and you'll get a mess of mixed results. Steve Jobs? Tim Cook? The system has no idea WHEN you're asking about. GraphRAG (Microsoft, 2024) tried to fix RAG with knowledge graphs and community detection. It's impressive — but it forgot something fundamental. Temporal RAG (TG-RAG, 2025) remembered what GraphRAG forgot: TIME. In this video, I break down: → Why traditional RAG systems are "time-blind" → How GraphRAG works (and where it fails) → The bi-level temporal graph architecture of TG-RAG → Why GraphRAG costs ~610K tokens per update vs TG-RAG's minimal cost → Real benchmark results (spoiler: TG-RAG wins across the board) → When to use GraphRAG vs when to use TG-RAG If you're building RAG systems with data that changes over time — financial reports, news, earnings calls, company docs — this is the video that'll save you months of headaches. Knowledge evolves. Your RAG should too. --- ⏱️ TIMESTAMPS 0:00 - Intro 0:46 - The Problem 1:31 - Graph RAG 1:31 - Graph RAG 3:12 - Temporal RAG 4:36 - Cost Comparison 5:28 - Benchmark 5:41 - When to use which? #GraphRAG #TemporalRAG #RAG #TGRAG #LLM #AI #MachineLearning #KnowledgeGraph #NLP #AIEngineering #Retrieval #Microsoft #TimeSeries #DataScience #ArtificialIntelligence