Learn PromptingIntroduction
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 Founder of Learn Prompting and an ML Researcher at the University of Maryland. He created the first open-source Prompt Engineering guide, reaching 3M+ people and teaching them to use tools like ChatGPT. Sander also led a team behind Prompt Report, the most comprehensive study of prompting ever done, co-authored with researchers from the University of Maryland, OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions. This 76-page survey analyzed 1,500+ academic papers and covered 200+ prompting techniques.