Understanding RAG: Basics, Challenges, and Improvements

#rag #llm #machinelearning
The LLMs can be improved by Retrieval Augmented Generation (RAG) with the integration of external information sources. RAG helps them to provide current responses, enhance contextualization, and access real-time data. RAG is a cost-effective option for a variety of applications as it avoids the need for periodic model retraining. In this video, we look into RAG basics, challenges and limitations.
Research Paper Summaries: • Simple Unsupervised Ke...
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About Me:
I am Prakhar Mishra and this channel is my passion project. I am currently pursuing my MS (by research) in Data Science. I have an industry work-ex of 3+ years in the field of Data Science and Machine Learning with a particular focus on Natural Language Processing (NLP).

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