LLMs, GPT, and Prompt Engineering for Developers
Published by Pearson
Unleash the power of LLMs and GPT through training models and prompt engineering
- Get started working with large-language models (LLMs) and GPT
- Train LLMs effectively
- Learn the art of prompt engineering
Dive into the fascinating world of Large Language Models (LLMs) and Generative Pre-trained Transformers (GPT) in this comprehensive live online course. Throughout four engaging hours, you'll learn the essentials of LLMs, GPT, and language modeling while exploring practical applications through prompt engineering. This course is perfect for programmers and software engineers interested in the cutting-edge technology that powers modern AI language understanding and generation.
This course is the first in a three-part series by Sinan Ozdemir designed for machine learning engineers and software developers who want to expand their skillset and learn how to work with LLMs like ChatGPT and FLAN-T5. The series provides practical instruction on prompt engineering, language modeling, moving LLM prototypes to production, and fine-tuning GPT models. The three live courses in the series are:
- LLMs, GPT and Prompt Engineering for Developers
- Using Open- and Closed-Source LLMs in Real World Applications
- LLMs from Prototypes to Production
The book Quick Start Guide to LLMs by Sinan Ozdemir is recommended as companion material for post-class reference.
What you’ll learn and how you can apply it
By the end of the live online course, you’ll understand:
- The architecture and functioning of LLMs and GPT
- The principles and applications of language modeling using LLMs
- How to effectively train and fine-tune LLMs
- The techniques involved in prompt engineering for various use cases
And you’ll be able to:
- Apply LLMs to real-world language modeling tasks
- Design prompts that elicit desired responses from LLMs
- Optimize LLM training for specific tasks and use cases
- Adapt LLMs for a variety of applications in natural language processing
This live event is for you because...
- You're curious about state-of-the-art AI language models and their applications
- You want to leverage LLMs and GPT technology for your projects
- You're interested in learning how to create effective prompts to interact with LLMs
- You seek to enhance your skills in natural language processing and AI
Prerequisites
- Attendees should have prior experience with machine learning and be proficient in Python programming.
- Familiarity with natural language processing concepts and techniques is helpful but not required.
- Attendees should have a willingness to engage in hands-on exercises and apply the concepts learned in the course to real-world applications.
Course Set-up
- Jupyter notebooks can be run alongside the instructor but students can follow along without coding with pre-run notebooks on the GitHub repository
- GitHub repository with the PDFs of slides, plus code and links in a Jupyter notebook: https://github.com/sinanuozdemir/pearson-llm-dev-intro
Recommended Preparation
- Watch: AI Catalyst Conference: NLP with ChatGPT (and other Large Language Models) by Jon Krohn
- Read: Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs by Sinan Ozdemir
- Watch: Introduction to Transformer Models for NLP by Sinan Ozdemir
Recommended Follow-up
- Watch: Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs) by Sinan Ozdemir
- Attend: Using Open- and Closed-Source LLMs in Real World Applications by Sinan Ozdemir
- Attend: LLMs from Prototypes to Production while Optimizing for Real-World Applications by Sinan Ozdemir
- Attend: Deploying GPT and Large Language Models by Sinan Ozdemir
- Explore: Getting Started with Data, LLMs and ChatGPT by Sinan Ozdemir
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Session 1: Large Language Models (LLMs) and Generative Pre-trained Transformers (GPT) (60 minutes)
- What are LLMs and GPT?
- Understanding how LLMs work
- GPT-2, GPT-3, and GPT-4 overview
Q&A (10 mins)
Break (10 minutes)
Session 2: Working with Pre-trained LLMs (60 minutes)
- Using open and closed source LLMs off the shelf
- Best practices for developing with LLMs
- Best practices for fine-turning LLMs
Q&A (10 mins)
Break (10 minutes)
Session 3: Prompt Engineering (60 minutes)
- Writing effective prompts
- Real-world prompt examples
- Engineering prompts for specific use cases
Introduction to Take Home Practice (5 minutes)
- These exercises are designed to solidify knowledge from the current session and prepare for subsequent sessions, should you choose to take them.
Q&A (15 mins)
Your Instructor
Sinan Ozdemir
Sinan Ozdemir is the founder of Crucible, an AI factory platform that helps teams convert existing workflows into custom models. He is a Y Combinator alum, AI & LLM Advisor at Tola Capital, and the author of multiple books on data science and machine learning including Building Agentic AI, Quick Start Guide to LLMs, and Principles of Data Science. Sinan is a former lecturer of data science at Johns Hopkins University and the founder of Kylie.ai, an enterprise-grade conversational AI platform (acquired 2014). He holds a master's degree in pure mathematics from Johns Hopkins University and is based in San Francisco, California.