🤔 Looking to enhance your RAG systems and save costs?
In this episode, join Angelina and Mehdi as they explore Generative Feedback Loops. Discover how this innovative technique can improve your AI’s performance, reduce bias, and create a self-evolving knowledge base.
Who’s Angelina: VP of AI and data, Co-founder of Transform AI Studio, two-time fast.ai fellows under Jeremy Howard, published author. https://www.linkedin.com/in/MeetAngelina/
Who’s Mehdi: Professor of Computer Science, Co-founder and Chief AI Engineer at Transform AI Studio, NSF fellow, published author. https://www.linkedin.com/in/mehdiallahyari/
What You’ll Learn:
🔄 How Generative Feedback Loops enhance RAG systems for continuous improvement
💡 Key benefits: adaptability, reduced bias, and personalized AI outputs
💰 Cost-saving strategies through efficient use of LLMs and long-term caching
🛠️ Practical tips for implementing Generative Feedback Loops in your RAG system
🚀 Creating self-improving AI knowledge bases that evolve with user interactions
✏️ In This Episode:
00:00 Intro
00:48 Basic RAG Architecture
01:19 What is Generative Feedback Loop?
05:27 Advantages for Generative Feedback Loop
05:39 Adaptability to New Data
07:06 Agentic Generative Feedback Loop
07:25 Bias and Error Reduction
09:16 Personalized Model Outputs
10:30 A Concrete Example
13:20 How to implement GFL system?
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🖼️ Blogpost for today:
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📚 If you’d like to learn more about RAG systems, check out our book on the RAG system: https://angelinamagr.gumroad.com/
🕴️ Our consulting firm: We help companies that don’t want to miss the boat of the current wave of AI advancement by integrating these solutions into their business operations and products. https://www.transformaistudio.com/
Stay tuned for more content! 🎥 Thanks you for watching! 🙌
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