GitHub Copilot Fundamentals Part 1 of 2
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Table of Content:
- Question 1: What is the ultimate goal of prompt engineering with GitHub Copilot?
- Question 2: What kind of AI model does GitHub Copilot use?
- Question 3: Why is iteration important when prompting Copilot?
- Question 4: What is meant by ‘assert and iterate’ when working with Copilot?
- Question 5: How do examples improve Copilot’s understanding?
- Question 6: Why is context important in prompts?
- Question 7: How can developers provide enough clarity in prompts?
- Question 8: What is meant by the ‘Surround’ principle?
- Question 9: What does the ‘Short’ principle refer to?
- Question 10: What does the ‘Specific’ principle emphasize?
- Question 11: What is zero-shot learning in GitHub Copilot?
- Question 12: What is one-shot learning?
- Question 13: What is few-shot learning?
- Question 14: What is the main advantage of using role prompting in GitHub Copilot?
- Question 15: How does the ‘Testing Specialist’ role assist developers?
- Question 16: What benefits does the ‘Performance Optimization’ role provide?
- Question 17: How does the ‘Security Expert’ role improve Copilot’s output?
- Question 18: What is role prompting?
- Question 19: What is the benefit of summarizing context in long Copilot sessions?
- Question 20: How can developers manage long conversations effectively?
- Question 21: What is the main challenge of long conversation histories with Copilot?
- Question 22: What is chain prompting?
- Question 23: What does the ‘Single’ principle mean?
- Question 24: What are the four principles of prompt engineering known as the ‘4 S’s’?
- Question 25: Why is prompt engineering important for GitHub Copilot users?
- Question 26: How can AI developers ensure reliability and safety?
- Question 27: What does Reliability and Safety mean in the context of AI?
- Question 28: How can fairness be ensured in AI systems?
- Question 29: What does the Fairness principle in AI emphasize?
- Question 30: What are Microsoft and GitHub’s Six Principles of Responsible AI?
- Question 31: Who defines the six key principles of Responsible AI?
- Question 32: What is the definition of Responsible AI?
- Question 33: How can organizations mitigate the risks associated with AI?
- Question 34: What are the main risks associated with Artificial Intelligence (AI)?
- Question 35: Why is Privacy and Security important in Responsible AI?
- Question 36: What are Microsoft and GitHub’s key practices for Privacy and Security in AI?
- Question 37: What does Inclusiveness mean in Responsible AI?
- Question 38: What is prompt engineering?
- Question 39: What is the overall goal of Responsible AI with GitHub Copilot?
- Question 40: Why is accountability becoming a critical issue in AI?
- Question 41: How do Microsoft and GitHub ensure Accountability in AI?
- Question 42: What does Accountability mean in Responsible AI?
- Question 43: How can developers make AI systems more transparent?
- Question 44: What does the Transparency principle in AI refer to?
- Question 45: What are examples of inclusive AI systems?
- Question 46: How does Microsoft promote inclusiveness in AI?
- Question 47: Why is it important to use AI responsibly in tools like GitHub Copilot?