Prompting for Developers Code, Data, and Debug with AI
AI Summary
Summary: Case Studies in Prompt Engineering Course
Instructor: Janini Ravi
Course Overview:
- Focus on various prompting techniques for generative AI.
- Covers data generation, code generation, prompt performance improvement, and debugging.
Key Topics Discussed:
- Data Generation Prompts:
- Instructions for generating synthetic data for datasets.
- Formats include SQL queries for easy database insertion.
- Code Generation Prompts:
- Capabilities of generative AI in writing and converting code.
- Techniques like starter code prompts and code explanations.
- Debugging code snippets using generative AI tools.
- Prompting Techniques:
- Zero-shot Prompting: No prior examples, relies on the model’s general knowledge.
- Few-shot Prompting: Provides examples to improve model responses.
- Chain of Thought Prompting: Guides models step-by-step for better problem-solving.
- Augmented Knowledge Prompting: Incorporates specific context to enhance model responses.
Practical Applications:
- Applications involve generating test data, code snippets, debugging, and forming structured responses.
- Examples provided throughout the course demonstrated the effectiveness of prompting strategies.
Conclusion:
- The course offers a solid understanding of leveraging generative AI through effective prompt engineering, enhancing the overall utility of tools like ChatGPT, Google Bard, and Bing Chat.