Generative Feedback Loops: The Secret Weapon for Supercharging Your RAG System

🤔 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.

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?

🖼️ Blogpost for today:

Stay tuned for more content! 🎥 Thanks you for watching! 🙌

[bonadio]

This is an interesting idea, but it needs extra care on the security side. If someone can jailbreak, now the bad result is added to the knowledge.

[awakenwithoutcoffee]

always nice seeing a new episode! Some topics for future could be:
- meta-data generation & filter : this seems the most useful method for improving RAG accuracy but its difficult to do e.g. filter based on "chapter", "topic" etc.
Also what if we just use a traditional data-base to store our data, vectorize the query to find meta-data most suited (domain specific meta-data database the LLM can pull from) -> retrieve parent documents based on meta-data: If we use a pre-defined schema that reflects all domain topics and classify/populate our documents with these meta-data than retrieval should be allot more accurate than vector searches. Just some thoughts as I'm still learning myself (especially struggling with meta-data generation as you can see ha!)

[souravbarua]

Thank you for this video. I learned a new thing. Very interesting and helpful.