Week 3 - Prompt Engineering & Productivity : Copilot Free learning & cert prep

[Akash1134]

👋 Welcome to Week 3 of the GitHub Copilot Free learning journey and cert prep! We're now at the halfway point in our learning journey, and it’s been incredible to see the depth of thought and creativity in your submissions so far.

This week, we’re shifting our focus to some of the core principles of prompt engineering and productivity. Understanding these foundational concepts will help you craft more effective prompts, allowing you to harness Copilot’s full potential for smarter, faster coding. Here's what we'll be covering to refine your skills and take your workflow to the next level 🚀

• Prompt Crating and Prompt Engineering
  • Fundamentals of prompt crafting
  • Fundamentals of prompt engineering
• Developer use cases for AI
  • Improve developer productivity
  • How GitHub Copilot can help with SDLC
  • Limitations of using GitHub Copilot
  • How to use the productivity API to see how GitHub Copilot impacts coding

Note

Don’t miss out—Keep engaging, learning, and growing together! Top participants will earn a GitHub Certifications exam voucher! 🎫 Think of it as your golden ticket to advancing your career! 💼

Your First Move: Study Smart, Level Up! 🎯

We've already explored Copilot’s features and data handling, giving you a solid foundation in how it works. Now, it's time to take your skills to the next level by mastering the art of prompt creation and prompt engineering. The way you frame your inputs directly impacts the quality of AI-generated outputs, and learning how to craft effective prompts will help you get the most out of Copilot. We'll also dive into real-world developer use cases, showing you practical ways Copilot can streamline your workflow, boost productivity, and enhance your coding experience.

• Microsoft Learn Modules (each of the modules has its own knowledge check exercise)
  • Introduction to prompt engineering with GitHub Copilot
  • Developer use cases for AI with GitHub Copilot
    • Using GitHub Copilot with JavaScript (Optional)
    • Using GitHub Copilot with Python (Optional)
  • Implement code improvements using GitHub Copilot tools
  • Develop code features using GitHub Copilot tools
• GitHub Learning Pathways
  • Essentials of GitHub Copilot (series)
• 🤩 Microsoft Ignite 2024 (Bonus)
  • Maximize developer efficiency with GitHub Copilot for partner success
  • Top 5 GitHub Copilot Features at Microsoft Ignite 2024
  • Discover GitHub Copilot's untapped potential in Visual Studio
  • GitHub Copilot's Game Changing Features
• Videos
  • Master the core principles of prompt engineering with GitHub Copilot
  • Visual Studio Prompt Engineering with GitHub Copilot
  • GitHub Copilot Study Guide Series 2: Developer use cases for AI with GitHub Copilot
  • Level up with GitHub Copilot: using AI to learn, code, and build
  • Getting started with GitHub Copilot (Playlist with over 80 videos)
• Blogs and Resources
  • Research: quantifying GitHub Copilot’s impact on developer productivity and happiness
  • What’s a prompt and what is prompt engineering?
  • What are the pros and cons of using GitHub Copilot?
  • How to use GitHub Copilot: Prompts, tips, and use cases
• GitHub Docs
  • Prompt engineering for GitHub Copilot
  • Use GitHub Copilot
  • Manage GitHub Copilot

Knowledge Checkpoint 🏁 - Let’s See What You’ve Got! 🧠

1. When using GitHub Copilot in a corporate environment, which of the following practices could introduce legal or security risks? (Select two)

A) Using Copilot to generate code for encryption algorithms without verification.
B) Enabling Copilot to suggest code completions within a private enterprise repository.
C) Accepting Copilot-generated code that lacks licensing information.
D) Using Copilot in an IDE that does not support secure API calls.

2. A developer is working with Copilot in Visual Studio and wants to optimize Copilot’s inline chat for debugging. What is the best way to achieve this?

A) Rewriting prompts to be more detailed, explicitly mentioning expected outputs.
B) Running Copilot Chat multiple times to get varied debugging suggestions.
C) Adjusting Copilot’s temperature parameter to control randomness in responses.
D) Restricting Copilot Chat to only use file-specific context rather than project-wide scope.

3. A developer is using GitHub Copilot to generate a function for sorting a large dataset (100,000+ entries). Copilot suggests the following function:

python
Copy
Edit
def sort_data(data):
    return sorted(data)

Why might this function be suboptimal for large datasets?
A) The sorted() function is not efficient for large datasets.
B) The function lacks an explicit sorting algorithm, making it unpredictable.
C) The function does not use an in-place sorting method like data.sort().
D) Copilot might suggest a less efficient sorting algorithm if not prompted explicitly.

4. A developer asks Copilot to generate a function that computes Fibonacci numbers. The suggested function uses recursion but lacks memoization. What is the most likely reason for this omission?

A) Copilot prioritizes standard implementations seen in open-source projects.
B) Copilot avoids generating computationally expensive algorithms.
C) Copilot lacks context about performance optimizations unless explicitly prompted.
D) Copilot assumes the user will manually optimize the generated code.

5. When using GitHub Copilot for SQL query generation, what should developers be cautious about?

A) Copilot always generates the most optimized query.
B) Copilot might suggest queries vulnerable to SQL injection.
C) Copilot does not support SQL query generation.
D) Copilot automatically optimizes database indexes.

6. A team is using Copilot for collaborative development. Developer A enables Copilot in a private repository. Developer B, working in the same repository, notices Copilot's suggestions contain code snippets that look unfamiliar. What might be happening?

A) Copilot is retrieving suggestions from similar open-source projects.
B) Copilot is generating code based on patterns detected in the project’s existing files.
C) Copilot is using external AI models to fetch relevant public code snippets.
D) Copilot is applying prompt engineering techniques to infer missing logic.

7. You request Copilot to generate a SQL query but notice that the suggested query lacks proper indexing considerations. What should you do?

A) Add a comment specifying performance constraints and indexing requirements.
B) Re-prompt Copilot with a more detailed query request.
C) Accept the query and manually optimize it.
D) Adjust Copilot’s model settings to prioritize efficiency.

8. A developer needs Copilot to generate optimized SQL queries. What prompt will yield the best results?

A) # Generate an SQL query to retrieve customer data.
B) # Write an optimized SQL query that retrieves customer data using an index on customer_id.
C) # Write an SQL query for fetching customer data using JOINs.
D) # Generate a basic SQL query to fetch all rows from the customers table.

9. A team is developing a proprietary algorithm and wants to ensure Copilot does not suggest code similar to public implementations. What is the best approach? (Select two)

A) Use private repositories with Copilot to limit exposure to public code patterns.
B) Explicitly describe unique constraints and design principles in inline comments.
C) Turn off Copilot suggestions for functions that contain proprietary logic.
D) Modify Copilot's training dataset to exclude certain open-source projects.

10. According to research on GitHub Copilot's impact, which of the following factors contribute most significantly to increased developer productivity when using Copilot? (Select two correct answers)

A) Reduction in the time spent searching for solutions on external websites.
B) Increased number of lines of code written per hour.
C) A higher frequency of completed pull requests per developer.
D) Faster onboarding of new developers due to AI-assisted code understanding.

11. Which of the following factors most significantly influence GitHub Copilot’s ability to generate high-quality completions? (Select two)

A) The recency of the training data used to build the model.
B) The user's coding patterns and past accepted suggestions.
C) The number of times the user has prompted Copilot in a single session.
D) Whether the user is working in an IDE or a browser-based editor.

12. You are using GitHub Copilot in Visual Studio Code and notice that it frequently suggests outdated API methods that have been deprecated. What are the most effective strategies to improve the relevance of suggestions? (Select two)

A) Add a comment explicitly mentioning the latest API version before the function definition.
B) Use natural language prompts that describe the intent of the code rather than function names.
C) Adjust the settings to restrict Copilot to suggestions from private repositories only.
D) Revert to an older version of GitHub Copilot that was trained on previous API versions.

13. A developer is building a machine learning model in Python. They ask GitHub Copilot to generate a function for model training but notice that Copilot repeatedly suggests suboptimal hyperparameter choices. What is the best way to guide Copilot to generate better completions?

A) Use inline comments to specify preferred hyperparameters and model architectures.
B) Accept a Copilot suggestion and manually tweak the parameters later.
C) Change the function name to include terms like “optimized” or “efficient.”
D) Train Copilot on previous successful models to improve its suggestions.

🚀 🚀 🚀

[paulsuv9]

Lets go! 🚀 🚀 🚀

[Ronaldo-001]

Lezzz go!!

[hemanthnutakki]

Lezzzz goo!!!!!

[hemanthnutakki]

I'm very excited. By understanding developer use cases, AI-driven productivity boosts, and Copilot’s role in SDLC, we can refine our workflows and make the most of its capabilities. We'll also explore Copilot’s limitations and how to leverage the productivity API to measure its impact.

[paulsuv9]

Great enthusiasm! I found that adding specific constraints in the prompt really helps Copilot generate more accurate code. How do you usually refine your prompts? 👍

[johnniekpng]

Go go go...

[Ronaldo-001]

This week was so nice and dug a lot into developer use cases for AI.

Here are my answers for this week:

1. When using GitHub Copilot in a corporate environment, which of the following practices could introduce legal or security risks? (Select two)

• ✅ A) Using Copilot to generate code for encryption algorithms without verification.
• ❌ B) Using Copilot-generated comments to explain complex logic.
• ✅ C) Accepting Copilot-generated code that lacks licensing information.
• ❌ D) Using Copilot to generate documentation for internal APIs.

Explanation:

Copilot-generated encryption algorithms might not be secure or follow industry best practices, leading to security vulnerabilities. Copilot can generate code without proper licensing information, leading to potential legal and intellectual property issues.

---

2. A developer is working with Copilot in Visual Studio and wants to optimize Copilot’s inline chat for debugging. What is the best way to achieve this?

• ✅ A) Rewriting prompts to be more detailed, explicitly mentioning expected outputs.
• ❌ B) Running Copilot in an isolated environment.
• ❌ C) Disabling Copilot's suggestions to avoid incorrect results.
• ❌ D) Avoiding Copilot's inline chat altogether for debugging.

Explanation:

Providing more detailed prompts helps Copilot generate more relevant debugging suggestions.

---

3. A developer is using GitHub Copilot to generate a function for sorting a large dataset (100,000+ entries). Why might this function be suboptimal for large datasets?

def sort_data(data):
    return sorted(data)

• ❌ A) The function uses Python’s built-in sorted() function.
• ❌ B) The function lacks proper error handling.
• ✅ C) The function does not use an in-place sorting method like data.sort().
• ❌ D) The function does not implement a custom sorting algorithm.

Explanation:

sorted(data)creates a new sorted list, increasing memory usage. Usingdata.sort()` sorts the list in place, reducing overhead.

---

4. A developer asks Copilot to generate a function that computes Fibonacci numbers. The suggested function uses recursion but lacks memoization. What is the most likely reason for this omission?

• ❌ A) Copilot avoids recursion in general.
• ❌ B) Copilot assumes performance optimizations are unnecessary.
• ✅ C) Copilot lacks context about performance optimizations unless explicitly prompted.
• ❌ D) Copilot generates inefficient code by default.

Explanation:

Copilot generates code based on common patterns, and unless explicitly prompted, it might not consider optimizations like memoization.

---

5. When using GitHub Copilot for SQL query generation, what should developers be cautious about?

• ❌ A) Copilot might use incorrect SQL syntax.
• ✅ B) Copilot might suggest queries vulnerable to SQL injection.
• ❌ C) Copilot does not support SQL queries.
• ❌ D) Copilot always ensures optimized query performance.

Explanation:

Copilot-generated SQL queries may not use parameterized queries, making them vulnerable to SQL injection.

---

6. A team is using Copilot for collaborative development. Developer A enables Copilot in a private repository. Developer B, working in the same repository, notices Copilot's suggestions contain code snippets that look unfamiliar. What might be happening?

• ❌ A) Copilot is pulling suggestions from external repositories.
• ✅ B) Copilot is generating code based on patterns detected in the project’s existing files.
• ❌ C) Copilot is accessing developer B’s personal GitHub repositories.
• ❌ D) Copilot is using AI-generated randomness.

Explanation:

Copilot suggests code based on the project’s context, including existing files in the repository.

---

7. You request Copilot to generate a SQL query but notice that the suggested query lacks proper indexing considerations. What should you do?

• ✅ A) Add a comment specifying performance constraints and indexing requirements.
• ❌ B) Disable Copilot’s SQL suggestions.
• ❌ C) Accept the generated query and optimize it later.
• ❌ D) Rely on Copilot to always generate optimized queries.

Explanation:

Explicitly defining performance needs in comments improves Copilot’s suggestions.

---

8. A developer needs Copilot to generate optimized SQL queries. What prompt will yield the best results?

• ❌ A) # Write a SQL query to fetch data.
• ✅ B) # Write an optimized SQL query that retrieves customer data using an index on customer_id.
• ❌ C) # Fetch customer data.
• ❌ D) # Generate a basic SQL query.

Explanation:

Providing specific requirements like "optimized" and "using an index on customer_id" ensures Copilot generates a more efficient query.

---

9. A team is developing a proprietary algorithm and wants to ensure Copilot does not suggest code similar to public implementations. What is the best approach? (Select two)

• ✅ A) Use private repositories with Copilot to limit exposure to public code patterns.
• ✅ B) Explicitly describe unique constraints and design principles in inline comments.
• ❌ C) Avoid using Copilot for proprietary code.
• ❌ D) Rely on Copilot’s built-in safeguards.

Explanation:

Private repositories prevent Copilot from using public code patterns in suggestions and providing unique constraints helps Copilot generate more original code.

---

10. According to research on GitHub Copilot's impact, which of the following factors contribute most significantly to increased developer productivity when using Copilot? (Select two)

• ✅ A) Reduction in the time spent searching for solutions on external websites.
• ❌ B) Copilot’s ability to replace all manual coding.
• ❌ C) Copilot’s ability to generate perfect code without human review.
• ✅ D) Faster onboarding of new developers due to AI-assisted code understanding.

Explanation:

Copilot reduces the need to search for external resources, speeding up development.
New developers onboard faster as Copilot assists in understanding and writing code.

---

11. Which of the following factors most significantly influence GitHub Copilot’s ability to generate high-quality completions? (Select two)

• ✅ A) The recency of the training data used to build the model.
• ✅ B) The user's coding patterns and past accepted suggestions.
• ❌ C) Copilot’s randomness.
• ❌ D) The number of programming languages Copilot supports.

Explanation:

More recent training data ensures Copilot generates relevant and up-to-date suggestions.
Copilot learns from user interactions, improving based on accepted completions.

---

12. You are using GitHub Copilot in Visual Studio Code and notice that it frequently suggests outdated API methods that have been deprecated. What are the most effective strategies to improve the relevance of suggestions? (Select two)

• ✅ A) Add a comment explicitly mentioning the latest API version before the function definition.
• ✅ B) Use natural language prompts that describe the intent of the code rather than function names.
• ❌ C) Disable Copilot and write the code manually.
• ❌ D) Ignore the suggestions and try again.

Explanation:

Mentioning the latest API version in comments helps Copilot generate up-to-date suggestions.
Describing the intent ensures Copilot understands the context better and suggests modern alternatives.

---

Also, This week resources are so good ! Thanks @Akash1134 for providing these resources!
Finally excited to share my project soon, which was built with the help of GitHub Copilot.

[SatyamSharma007]

Question: When using GitHub Copilot in a corporate environment, which of the following practices could introduce legal or security risks? (Select two)

✅ A) Using Copilot to generate code for encryption algorithms without verification.

✅ C) Accepting Copilot-generated code that lacks licensing information.

Question: A developer is working with Copilot in Visual Studio and wants to optimize Copilot’s inline chat for debugging. What is the best way to achieve this?

✅ A) Rewriting prompts to be more detailed, explicitly mentioning expected outputs.

Question: A developer is using GitHub Copilot to generate a function for sorting a large dataset (100,000+ entries). Why might this function be suboptimal for large datasets?

✅ C) The function does not use an in-place sorting method like data.sort().

Question: A developer asks Copilot to generate a function that computes Fibonacci numbers. The suggested function uses recursion but lacks memoization. What is the most likely reason for this omission?

✅ C) Copilot lacks context about performance optimizations unless explicitly prompted.

Question: When using GitHub Copilot for SQL query generation, what should developers be cautious about?

✅ B) Copilot might suggest queries vulnerable to SQL injection.

Question: A team is using Copilot for collaborative development. Developer A enables Copilot in a private repository. Developer B, working in the same repository, notices Copilot's suggestions contain code snippets that look unfamiliar. What might be happening?

✅ B) Copilot is generating code based on patterns detected in the project’s existing files.

Question: You request Copilot to generate a SQL query but notice that the suggested query lacks proper indexing considerations. What should you do?

✅ A) Add a comment specifying performance constraints and indexing requirements.

Question: A developer needs Copilot to generate optimized SQL queries. What prompt will yield the best results?

✅ B) # Write an optimized SQL query that retrieves customer data using an index on customer_id.

Question: A team is developing a proprietary algorithm and wants to ensure Copilot does not suggest code similar to public implementations. What is the best approach? (Select two)

✅ A) Use private repositories with Copilot to limit exposure to public code patterns.

✅ B) Explicitly describe unique constraints and design principles in inline comments.

Question: According to research on GitHub Copilot's impact, which of the following factors contribute most significantly to increased developer productivity when using Copilot? (Select two)

✅ A) Reduction in the time spent searching for solutions on external websites.

✅ D) Faster onboarding of new developers due to AI-assisted code understanding.

Question: Which of the following factors most significantly influence GitHub Copilot’s ability to generate high-quality completions? (Select two)

✅ A) The recency of the training data used to build the model.

✅ B) The user's coding patterns and past accepted suggestions.

Question: You are using GitHub Copilot in Visual Studio Code and notice that it frequently suggests outdated API methods that have been deprecated. What are the most effective strategies to improve the relevance of suggestions? (Select two)

✅ A) Add a comment explicitly mentioning the latest API version before the function definition.

✅ B) Use natural language prompts that describe the intent of the code rather than function names.

Question: A developer is building a machine learning model in Python. They ask GitHub Copilot to generate a function for model training but notice that Copilot repeatedly suggests suboptimal hyperparameter choices. What is the best way to guide Copilot to generate better completions?

✅ A) Use inline comments to specify preferred hyperparameters and model architectures.

[paulsuv9]

Great answers! I agree with your points. What do you think are the biggest challenges when using Copilot for large-scale projects? 🚀

[kalharatennakoon]

Hey @Akash1134 👋
Here are my answers:

1. A & C
2. A
3. C
4. C
5. B
6. B
7. A
8. B
9. A & B
10. A & D
11. A & B
12. A & B
13. A

[paulsuv9]

Nice work! One tip I found useful this week is to use natural language prompts that describe the intent of the code rather than specific function names. It helps Copilot understand the context better. 👍

[Akash1134]

Join the Prompt battle 🤜

💡 Challenge:

Think you can out-prompt GitHub Copilot? Let’s put your prompt engineering skills to the test! Pick a simple coding task and refine your prompt step by step to get the best possible output from Copilot.

How to Participate:

1. Choose a simple task (e.g., "Write a function to reverse a string").
2. Start with a basic prompt and share Copilot's first response.
3. Refine your prompt to make Copilot’s response more efficient, readable, or creative—share the improvements!
4. Bonus: Engage with another participant’s attempt --> can you suggest an even better prompt?

The best one wins the battle ✌️

Example:

• ❌ Basic prompt: "Reverse a string in Python." → Copilot gives a simple function.
• ✅ Improved prompt: "Write a Python function to reverse a string using recursion, with docstrings and edge case handling." → Copilot generates a more optimized version!

Share your Promptness below👇 (we have time till Monday next week)

[Sirius-ashwak]

Here are my answers! @Akash1134

1. A & C
2. A
3. C
4. C
5. B
6. A
7. A
8. B
9. A & B
10. A & D
11. A & B
12. A & B
13. A

[paulsuv9]

I’m very excited about this week’s focus on prompt engineering! One tip I found useful is to be very specific with prompts to get the best results from Copilot. Has anyone else noticed a significant improvement in their code quality? 🚀

[paulsuv9]

It might be helpful to add a section on common pitfalls to avoid when using Copilot for prompt engineering.