Prompt Engineering Tutorial – Master ChatGPT and LLM Responses
Learn prompt engineering techniques to get better results from ChatGPT and other LLMs.
For more information about Stanford’s Artificial Intelligence programs visit: https://stanford.io/ai https://www.youtube.com/watch?v=Bl4Feh_Mjvo To follow along with the course, visit:https://cs229.stanford.edu/syllabus-s… Tengyu MaAssistant Professor of Computer Sciencehttps://ai.stanford.edu/~tengyuma/ Christopher RéAssociate Professor of Computer Sciencehttps://cs.stanford.edu/~chrismre/
Demystifying attention, the key mechanism inside transformers and LLMs.
Chapters0:00 Introduction1:54 Neural N-Gram Models6:03 Recurrent Neural Networks11:47 LSTM Cells12:22 Outro
Get ready for a showdown between LangChain and LangGraph, two powerful frameworks for building applications with large language models (LLMs.) Master Inventor Martin Keen compares the two, taking a look at their unique features, use cases, and how they can help you create innovative, context-aware solutions.
Timestamps:0:00 – Who this was made for0:41 – What are large language models?7:48 – Where to learn more
This is the 5th video in a series on using large language models (LLMs) in practice. Here, I discuss how to fine-tune an existing LLM for a particular use case and walk through a concrete example with Python code.