Using GraphRAG to Generate Business Insights

In this episode, guest presenter Alison Cossette, Developer Advocate at Neo4j, will explore GraphRAG, a powerful technique that combines Knowledge Graphs and Retrieval-Augmented Generation (RAG) to enhance AI's ability to provide accurate, context-rich responses.

Alison cover what defines a high-quality dataset for RAG, how to identify potential blind spots in your data, and how to leverage these insights to drive business outcomes.

By the end of the episode, you’ll have a clearer understanding of how to apply GraphRAG to unlock valuable insights and optimize decision-making in your organization.

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Chapters
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00:00:00 - Open
00:00:24 - Welcome + Overview
00:01:40 - Interview with Alison Cossette
00:04:18 - Session Agenda
00:05:02 - What is RAG?
00:07:03 - What to use for External Data?
00:08:04 - Components of a Knowledge Graph
00:09:55 - Your Business Needs
00:10:50 - Modeling Your Data
00:12:00 - Uncover Patterns in Your Customer Data
00:16:42 - GraphRAG Overview
00:17:50 - AI Explainability
00:19:20 - Explainability Conversation
00:22:28 - Semantic Search and Traversal
00:25:01 - GraphRAG Sample Workflow
00:26:31 - Start with a High Quality Dataset
00:30:08 - Structured Content Conversation
00:32:05 - Logging Conversations with LLM
00:36:32 - Conversation Lengths
00:38:03 - LLM Response Time
00:38:28 - Visualizing Context Document Usage
00:41:17 - Analyzing LLM Responses
00:42:33 - Summary
00:43:45 - Closing Interview with Alison Cossette
00:46:01 - Closing
00:46:44 - Credits

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Learn how to use Gen-AI, RAG, Knowledge Graphs, Headless CMS, Content Modeling, Structured Content and more so that you can transform your content into intelligent content? Visit https://www.intelligencontent.academy