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NB 30 - Addressing the AI Hallucination Problem
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Content provided by Geoff Livingston and Greg Verdino, Geoff Livingston, and Greg Verdino. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Geoff Livingston and Greg Verdino, Geoff Livingston, and Greg Verdino or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Special guest Eyelevel.ai CEO Neil Katz joins Geoff and Greg to discuss generative AI's hallucination problem. Large language models' (LLMs') propensity to hallucinate in their answers represents one of the biggest barriers to enterprise adoption. Eyelevel boasts a 95% accuracy on private instance responses by using its APIs and tools to prepare proprietary data for LLM consumption.
The trio dives into the LLM marketplace, including discussions about why brands choose to implement a private instance, how the LLM market has evolved, and what causes the hallucination problem. Then, they discuss the enterprise data problem and how retrieval augmented generation (RAG) techniques still need additional help to strengthen LLM responses.
Chapters include:
0:00 Start
4:40 Private instance versus licensing enterprise editions of LLMs
7:32 Eyelevel’s Air France implementation achieving 95% success rates
12:29: The need for enterprise data preparation
18:14 The hallucination problem with LLMs and RAG approaches
28:23 How governance can or cannot help enterprises
32:32 Why some use open source versus proprietary LLMs
39:12 The future of AI and an incredible vision
Learn more about Eyelevel at https://www.eyelevel.ai/ or contact Neil Katz via LinkedIn at https://www.linkedin.com/in/neilkatz/
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