ChatGPT Prompt Engineering for Developers - Syllabus

• Introduction to Prompt Engineering and LLMs
  • Overview of large language models (LLMs) and their capabilities
  • Importance of prompt engineering in application development
  • Brief introduction to using the OpenAI API
• Guidelines for Prompt Engineering
  • Best practices for creating effective prompts
  • Key principles of prompt engineering for optimal results
  • Hands-on code examples to illustrate effective prompt design
• Iterative Prompt Development
  • Techniques for iterating on prompts to improve accuracy and relevance
  • Understanding the process of refining prompts for complex tasks
  • Practical code examples to develop prompts in stages
• Prompting for Text Summarization
  • Using LLMs for summarizing content, such as user reviews
  • Techniques for creating concise and relevant summaries
  • Code examples to explore summarization in different contexts
• Prompting for Text Inference
  • Inferring sentiments, topics, and categories from text
  • Applications of inference, including sentiment classification and topic extraction
  • Code examples demonstrating inference tasks with LLMs
• Prompting for Text Transformation
  • Transforming text for tasks such as translation, spelling, and grammar correction
  • Techniques for structuring prompts to modify text effectively
  • Hands-on practice with code examples on text transformation
• Prompting for Text Expansion
  • Expanding text to generate longer outputs, such as emails
  • Applications for automated content creation and email generation
  • Practical examples for generating expanded text outputs
• Building a Custom Chatbot
  • Steps to create a chatbot using LLM prompts
  • Structuring prompts to enable conversational responses
  • Code examples for building and customizing a chatbot
• Course Conclusion
  • Summary of key takeaways in prompt engineering
  • Final tips for effective use of LLMs in application development