Primer to RAG Functioning and LLM Architecture: Pre-trained and Fine-tuned LLMs

Welcome back to our module on LLM Architecture and RAG!

Up next is a series of learning resources created by Anup Surendran that sets the stage for your journey ahead. This video serves as a primer, acquainting you with key concepts such as pre-training, RLHF (Reinforcement Learning from Human Feedback), fine-tuning, and in-context learning.

These aren't just buzzwords; they're your toolkit for unlocking the full potential of Large Language Models. Understanding these terms will be crucial as they lay the groundwork for our upcoming module, which delves into 'In-Context Learning.' So, stay tuned!

Resources for Later: Once you've completed this module on RAG and LLM Architecture, you can explore these resources for a more comprehensive understanding of RLHF.

• For a beginner-friendly introduction, check out the video by HuggingFace, which offers an accessible explanation of RLHF concepts.

• Additionally, for a slightly deeper dive focusing on the mathematical aspects, consider the lecture on RLHF available by Stanford Online on their YouTube channel.