Prompt Engineering for ChatGPT

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What Will You Learn?

How to apply prompt engineering to effectively work with large language models, like ChatGPT
How to use prompt patterns to tap into powerful capabilities within large language models
How to create complex prompt-based applications for your life, business, or education

About This Course

Provider: Coursera
Format: Online
Duration: 18 hours to complete [Approx]
Target Audience: Beginners
Learning Objectives: By the end of this free course, students will have strong prompt engineering skills and be capable of using large language models for a wide range of tasks in their job, business, personal life, and education, such as writing, summarization, game play, planning, simulation, and programming.
Course Prerequisites: NA
Assessment and Certification: Earn a Certificate upon completion from the relevant Provider
Instructor: Vanderbilt University
Key Topics: Prompt Engineering, ChatGPT, Chain Of Thought Prompting, Prompt Patterns, Large Language Models
Topic Covered: 
  1. - Overview of the 
  2. - Motivating Example: Act as a Speech 
  3. - Setting Up an Account and Using 
  4. - What are Large Language Models?
  5. - Randomness in Output
  6. - What is a Prompt?
  7. - Intuition Behind 
  8. - Everyone Can Program with 
  9. - Prompt 
  10. - The Persona 
  11. - Introducing New Information to the Large Language 
  12. - Prompt Size 
  13. - Prompts are a Tool for Repeated 
  14. - Root Prompts
  15. - Question Refinement 
  16. - Cognitive Verifier 
  17. - Audience Persona 
  18. - Flipped Interaction Pattern
  19. - Few-shot 
  20. - Few-shot Examples for 
  21. - Few-Shot Examples with Intermediate 
  22. - Writing Effective Few-Shot 
  23. - Chain of Thought 
  24. - ReAct 
  25. - Using Large Language Models to Grade Each Other
  26. - Game Play 
  27. - Template 
  28. - Meta Language Creation 
  29. - Recipe 
  30. - Alternative Approaches Pattern
  31. - Ask for Input 
  32. - Combining 
  33. - Outline Expansion 
  34. - Menu Actions 
  35. - Fact Check List 
  36. - Tail Generation 
  37. - Semantic Filter 
  38. - Course Conclusion

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