# GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681

Page: https://stenobird.com/podcast/twiml-ai-podcast/graphrag-knowledge-graphs-for-ai-applications-with-kirk-marple-681
Text version: https://stenobird.com/podcast/twiml-ai-podcast/graphrag-knowledge-graphs-for-ai-applications-with-kirk-marple-681.md
Podcast: [The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)](https://stenobird.com/podcast/twiml-ai-podcast)
Published: 2024-04-22T18:58:00+00:00
Episode link: https://twimlai.com/podcast/twimlai/graphrag-knowledge-graphs-for-ai-applications/
Audio file: https://pscrb.fm/rss/p/traffic.megaphone.fm/MLN3405997576.mp3?updated=1713836204
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JSON: https://stenobird.com/v1/public/podcasts/twiml-ai-podcast/episodes/graphrag-knowledge-graphs-for-ai-applications-with-kirk-marple-681
Duration seconds: 2828

## Resource

Today we're joined by Kirk Marple, CEO and founder of Graphlit, to explore the emerging paradigm of "GraphRAG," or Graph Retrieval Augmented Generation. In our conversation, Kirk digs into the GraphRAG architecture and how Graphlit uses it to offer a multi-stage workflow for ingesting, processing, retrieving, and generating content using LLMs (like GPT-4) and other Generative AI tech. He shares how the system performs entity extraction to build a knowledge graph and how graph, vector, and object storage are integrated in the system. We dive into how the system uses “prompt compilation” to improve the results it gets from Large Language Models during generation. We conclude by discussing several use cases the approach supports, as well as future agent-based applications it enables. The complete show notes for this episode can be found at twimlai.com/go/681.

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