Retrieval Augmented Generation (RAG): A Hands-On Tutorial!

New to the world of Retrieval Augmented Generation (RAG)? No worries, this video should help you understand the basics with a hands-on tutorial.
Large Language Models (LLMs) sometimes produce hallucinated answers and one of the techniques to mitigate these hallucinations is by RAG. For an user query, RAG tends to retrieve the information from the provided source/information/data that is stored in a vector database. A vector database is the one that is a specialized database other than the traditional databases where vector data is stored. Vector data is in the form of embeddings that captures the context and meaning of the objects.
Try the RAG tutorial,
The complete notebook code is here: github.com/pavanbelagatti/RAG...
Sign up to SingleStore for free to get started: bit.ly/3Y2I4cV
Here is my complete article on RAG: bit.ly/3LjRNE8

[@TechPuzzle_Haven]

Great video..Thanks alot.

[@pavanbelagatti]

Glad it was helpful. Thanks for the support:)

[@user-ht5ev7il3h]

Please do Advanced rag , hybrid rag agentic rag using Langchain ecosystem

[@pavanbelagatti]

Yes, I am planning to do something on agentic RAG. You will see the video soon. Thanks for the support:)