Introduction to Large Language Models (LLMs) and Prompt Engineering

Learn how to use and launch large language models (LLMs) like GPT, Llama, Claude T5, and BERT and design prompts for optimal AI workloads.

Introduction to Large Language Models (LLMs) and Prompt Engineering guides you to launch LLMs like GPT, Llama, Claude, T5, and BERT at scale. It presents a step-by-step approach to building and deploying LLMs, with real-world case studies to illustrate the concepts. It also covers how to begin your LLM journey with prompt engineering with optimal instruction placements and prompting across models. The video works toward building a Retrieval-Augmented Generation (RAG) system with LLMs. It fills a gap in the market by providing a guide to using LLMs and will be a valuable resource for anyone looking to use LLMs in their projects.

The companion GitHub repository for this course is at https://github.com/sinanuozdemir/quick-start-guide-to-llms

About the Instructor

Sinan Ozdemir is founder and CTO of LoopGenius, where he uses state-of-the-art AI to help people create and run their businesses. He has lectured in data science at Johns Hopkins University and authored multiple books, videos, and numerous online courses on data science, machine learning, and generative AI. He also founded the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. Sinan most recently published Quick Guide to Large Language Models, 2nd Edition, and launched a podcast audio series, AI Unveiled. Ozdemir holds a master’s degree in pure mathematics from Johns Hopkins University.

Learn How To:

Apply large language models (LLMs) and use semantic search with them
Utilize principles of prompt engineering to build agents and a retrieval-augmented generation (RAG) bot with OpenAI and GPT-4
Understand how AI agents are built and operated
Who Should Take This Course:

Machine learning engineers with experience in ML and want to learn more about LLMs
Developers, data scientists, and engineers who are interested in using LLMs for their projects
Those who want the best outputs from Generative LLMs and Embedding models
Skill Level:

Beginner to Intermediate
Course requirement:

Python 3 proficiency with some experience working in interactive Python environments including Notebooks (Jupyter/Google Colab/Kaggle Kernels)