When ChatGPT was first released, there was talk it would lead to traditional search engines, like Google, soon becoming obsolete. That was until users discovered generative AI’s one major drawback – it makes stuff up.

Because of the stochastic nature of ChatGPT, it is never going to be possible to completely eliminate hallucinations. However, there are ways to work around this issue. One such way is through leveraging knowledge graphs and retrieval augmented generation (or RAG).

In this episode, Kirk Marple joins Dr Genevieve Hayes to discuss how knowledge graphs and RAG can be leveraged to improve the quality of generative AI.

Guest Bio

Kirk Marple is the CEO and Technical Founder of Graphlit, serverless, cloud-native platform that streamlines the development of AI apps by automating unstructured data workflows and leveraging retrieval augmented generation.

Highlights

• (00:19) Meet Kirk Marple
• (01:22) Leveraging knowledge graphs and RAG
• (06:08) Challenges with named entity extraction
• (09:16) Cost implications of LLMs
• (12:17) Deep dive into RAG
• (16:58) Vector search explained
• (20:49) Graph databases and RAG
• (38:58) Future of RAG and AI
• (43:08) Final thoughts

Links

• Connect with Kirk on LinkedIn
• Graphlit website
• Follow Graphlit on X
• Connect with Genevieve on LinkedIn
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