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 Over a period of time they tend to become outdated if not trained on latest data.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

[shanmugasuntharamsankarali]

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

[MrSaiAarya]

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

[hemanth]

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

[MadhushreeSinha]

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

[jinmina]

This is very good presentation on necessity of RAG. 👍

[qgbobdx]

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

[nvaidya]

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

[srikanthtm]

Nicely explained. Thank you for the article.