Retrieval Augmented Generation (RAG) | Embedding Model, Vector Database, LangChain, LLM

Large language models provide great answers, but they're limited to the data they have been trained on. Over a period of time they tend to become outdated if not trained on latest data.
We can use "Retrieval-Augmented Generation" or RAG to augment the LLMs knowledge - so that they can work with relevant latest or proprietary data. In this video we go over the concepts of RAG, Vector Databases and LangChain - which is a opensource framework to implement RAG.
This is an Introduction to Retrieval Augmented Generation.
#artificialintelligence #langchain #llm #rag #RetrievalAugmentedGeneration #NLP

[@hemanth3092]

Excellent content on RAG and vector DB with simple flow steps...thanks

[@nasamind]

Awesome

[@MrSaiAarya]

One of the best, simple and clear explanation of the concept. Awesome job !!

[@shanmugasuntharamsankarali8108]

I had no idea about the details of LLM, inspite of that, I was able to understand RAG.. Thank you

[@MadhushreeSinha]

After so many videos I found this. It’s really simple and effective way to describe RAG architecture overview. ❤

[@ArchitectureBytes]

Glad it was helpful!

[@jinmina]

This is very good presentation on necessity of RAG. 👍

[@user-qg5bo9bd5x]

thanks for explaining concisely ! no fluff. to the point !!

[@nvaidya2050]

Excellent concise & to-the-point presentation. Thank you!

[@ArchitectureBytes]

Glad you liked it!

[@krishnakumarshetti9334]

Wonderfully explained! Thanks.