A Deep Dive into Retrieval Augmented Generation - AI PM Community Session #37
Become an AI product manager:
https://www.productmanagementexercises.com/ai-ml-product-manager?utm_source=youtube&utm_medium=referal
In our 37th session, Neeraj Sujan guided us through an in-depth exploration of RAGs and Advanced RAGs. He delved into their architectures, demonstrated how to select an appropriate retrieval model, and discussed the intricacies of Embeddings and Vector Databases. Furthermore, Neeraj addressed the challenges encountered, and provided insights into the pipeline implementation, among other topics.
If you wish to participate in our community sessions, we are offering our AI PM community sessions for free and open to the public every Saturday at 9:30 AM PST. Don't miss out on this incredible opportunity to grow in the AI product management field.
Visit the AI PM Community sessions page to learn more:
https://www.productmanagementexercises.com/Public-AI-Product-Management-Community-Sessions?utm_source=youtube&utm_medium=referal
Become a world-class AI Product Manager!
Join our 4-week live online program with a small group of other product managers, learn the necessary concepts for navigating through the AI/ML space and being an effective PM, get year-round access to expert workshops, learning material, and coaching to help you become a great AI/ML product manager, and gain lifetime access to a community of high-caliber peers for networking and support in the AI/ML community.
Visit the AI/ML Product Management program to learn more:
https://www.productmanagementexercises.com/ai-ml-product-manager?utm_source=youtube&utm_medium=referal
Timestamps:
00:00:00 Intro
00:00:53 Building a Large Language Model with RAGs
00:06:58 How Do We Benchmark Our Algorithms?
00:10:29 Choosing a Vector Database
00:20:09 Different Embedding Algorithms in Google
00:21:38 RAGs in the AI Prompt Engineering
00:30:33 RAGs in the OpenAI World
00:37:16 Building a Meaningful Content System
00:40:22 RHLF and the Pricing
00:45:21 Additional Reading Material for the Co
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Chapters (10)
Intro
0:53
Building a Large Language Model with RAGs
6:58
How Do We Benchmark Our Algorithms?
10:29
Choosing a Vector Database
20:09
Different Embedding Algorithms in Google
21:38
RAGs in the AI Prompt Engineering
30:33
RAGs in the OpenAI World
37:16
Building a Meaningful Content System
40:22
RHLF and the Pricing
45:21
Additional Reading Material for the Co
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Tutor Explanation
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