Supercharging RAG with Generative Feedback Loops from Weaviate

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How to supercharge RAG applications? With Generative Feedback Loops: feed data from a database to your LLM, store the outputs back into the database with a vector embedding! Then, search through the generated data in near real-time for future applications.
In this video, we explain RAG, Generative Feedback Loops, give examples of applications that require them, and show you how to implement them with @Weaviate .

Outline:
00:00 Generative Feedback Loops Motivation
01:32 RAG explained
03:21 Generative Feedback Loops
05:09 Concrete example with Weaviate code
08:30 DSPy: More Applications of Generative Feedback Loops
10:22 Conclusion, Weaviate Sponsor

Thanks to our Patrons who support us in Tier 2, 3, 4: 🙏
Dres. Trost GbR, Siltax, Vignesh Valliappan, Michael, Sunny Dhiana, Andy Ma

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Video editing: Nils Trost
Music 🎵 : Cru – Yung Logos

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Комментарии

Cool! I love these hands-on videos with code examples! Thanks for sharing the notebook!

DerPylz

Are there any LLM's out there that are specialized on teaching(potential students) and optimized by collaborating with people that are specialists in pedagogical frameworks and pedagogical tools?

encapsulatio

Thanks for the video. So, is Weaviate a database service that is leveraging the LLM to generate natural language queries to access the data? So, basically rather than hiring an SQL programmer, you just feed Weaviate the data (somehow), and use the LLM to gen the queries, which are then fed back to Weaviate so it can provide the query results?

marcfruchtman

but is the benefit worth the potential negatives? surely, there are tradeoffs here.

wryltxw

nice video. I am from India. I am working in the AI field. do you have any remote opportunities for me?

TemporaryForstudy

Hey letitia. I think you should consider changing the logo. It kinda looks like a turd

elpepemandioca

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