Introduction
Explore practical GenAI implementations, including writing emails, document generation, code synthesis, and content optimization.
Having established fundamental prompt engineering principles, we'll now focus on applying these techniques to solve real-world problems. This section bridges theoretical knowledge with practical applications, demonstrating how to leverage LLMs (Large Language Models) effectively in your daily workflow.
We'll explore concrete applications of GenAI across various domains, including:
- Email composition
- Document generation and processing
- Content optimization
- and more
Each application will be accompanied by specific examples, prompt templates, and best practices to ensure consistent, high-quality outputs. You'll learn how to fine-tune your prompts for different use cases and understand the limitations and capabilities of current LLM technology.
These examples utilize various LLM implementations, including ChatGPT. While the specific model choice may affect output quality or capabilities, the fundamental prompt engineering principles remain consistent across platforms.
Sander Schulhoff
Sander Schulhoff is the CEO of HackAPrompt and Learn Prompting. He created the first Prompt Engineering guide on the internet, two months before ChatGPT was released, which has taught 3 million people how to prompt ChatGPT. He also partnered with OpenAI to run the first AI Red Teaming competition, HackAPrompt, which was 2x larger than the White House's subsequent AI Red Teaming competition. Today, HackAPrompt partners with the Frontier AI labs to produce research that makes their models more secure. Sander's background is in Natural Language Processing and deep reinforcement learning. He recently led the team behind The Prompt Report, the most comprehensive study of prompt engineering ever done. This 76-page survey, co-authored with OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions, analyzed 1,500+ academic papers and covered 200+ prompting techniques.