In this episode, Anthony Alcaraz (Chief Product Officer) and Andreas Welsch discuss supercharging your Retrieval Augmented Generation (RAG) application with a knowledge graph. Anthony shares his experience expanding Generative AI applications and large language models and provides valuable tips for listeners looking to get even more relevant results from their AI applications.Key topics:- Identify shortcomings of Retrieval Augmented Generation (RAG)- Describe a knowledge graph and its purpose- Learn how to build a knowledge graph- Clarify when to use a knowledge graphListen to the full episode to hear how you can:- Provide context between data sources with a knowledge graph- Gather and prepare qualitative data to build your knowledge graph and AI model on- Treat knowledge graphs as one concept among others in your AI strategy- Start with the business problem and identify the best data sources and methods for solving itWatch this episode on YouTube:https://youtu.be/OSj08YBdQrgQuestions or suggestions? Send me a Text Message. Support the show***********Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.Level up your AI Leadership game with the AI Leadership Handbook:https://www.aileadershiphandbook.comMore details: https://www.intelligence-briefing.com All episodes:https://www.intelligence-briefing.com/podcastGet a weekly thought-provoking post in your inbox:https://www.intelligence-briefing.com/newsletter

Ask this episode

“What did the council decide on item 42?” — answered with the speaker and the moment.

Transcript

Related