In this episode, Lucas and Luna explore how knowledge graphs are supercharging retrieval-augmented generation (RAG) systems. They break down a concrete example: how a financial services firm used a knowledge graph built from SEC filings and earnings call transcripts to reduce hallucination in their Q&A chatbot by 40 percent. The hosts explain why flat vector search alone often fails, how graph traversal adds context, and what it takes to maintain a dynamic knowledge graph. They also touch on trade-offs like latency and engineering complexity. If you've wondered when RAG needs more than a vector database, this episode gives you the practical answer.

#KnowledgeGraphs #RAG #RetrievalAugmentedGeneration #Hallucination #VectorSearch #GraphTraversal #Neo4j #SECFilings #EarningsCalls #NLP #LLMOps #DataScience #Technology #FexingoBusiness #BusinessPodcast #MachineLearning #GraphDB #EntityResolution

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