Boost Efficiency with 17 Expert Copilot Programming Techniques

In today’s fast-paced technological world, efficient coding practices are more than a mere desire — they’re an absolute necessity. Enter GitHub Copilot, an advanced AI-driven tool to enhance and streamline the coding experience. But what is Copilot, and how has it etched its importance in the modern coding narrative? Let’s delve into it.

The importance of efficient coding practices

Coding is akin to constructing a building. A shaky foundation or haphazard approach might lead to a faulty end product, wasting countless hours in debugging and restructuring.

The financial burden due to inefficient coding practices is estimated to cost the global economy $85 billion annually.

Copilot: Your New Coding Wingman

GitHub Copilot, developed in collaboration with OpenAI, is not just another tool in the developer’s arsenal — it’s akin to having an expert copilot beside you, guiding, suggesting, and, at times, even taking control to ensure the journey (read: coding project) is as smooth as possible.

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The rise of Copilot programming

Before the advent of AI-driven tools, coding was primarily a manual endeavor, restricted to expertise, experience, and, occasionally, the ability to “Google efficiently.” However, with the explosion of AI and machine learning, tools like Copilot have transformed coding from a mere manual task to an intelligent, interactive experience.

Why every developer should consider Copilot programming techniques

In a rapidly advancing tech world, the right tools and techniques can be the difference between staying ahead and leaving behind. But why exactly should developers adopt Copilot programming techniques?

Boosts coding speed

One of the primary benefits of Copilot is its ability to reduce coding time drastically. Instead of starting from scratch or endlessly scrolling through Stack Overflow, developers can get direct suggestions that fit their coding context.

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Reduces bugs and improves code quality

A common challenge in coding is the introduction of bugs. Copilot, trained on various code repositories, offers generally optimized and less error-prone suggestions.

Top 17 Copilot programming techniques

To truly harness the power of Copilot, it’s crucial to understand its extensive range of capabilities. Let’s explore some expert-endorsed techniques.

1. Setting up code context

Copilot operates best when it understands your intentions. Setting the context allows the AI to give precise and targeted code suggestions.

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2. Iterative Prompting

One unique feature of Copilot is its iterative nature. If the initial code suggestion isn’t a perfect fit, refining your prompt or asking repeatedly can generate varied solutions to fit your needs.

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3. Comment-first coding

One of the most popular techniques to extract precise code suggestions from Copilot is by starting with comments. Describing your intent in the comment can help Copilot generate the exact code snippet you aim for.

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4. Using Pre-defined Templates

Harness the power of templates to simplify repetitive tasks. Instead of coding the same structures repeatedly, let Copilot fill in the blanks.

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5. Reviewing and Refactoring

GitHub Copilot is powerful, but it’s not flawless. After getting a code suggestion, it’s crucial to review and sometimes refactor it to fit the specific needs of your project.

6. Combine with traditional coding

Remember, Copilot is a tool, not a replacement. Balancing its suggestions with your expertise to craft optimized and efficient code is essential.

7. Utilizing shortcuts and commands

Getting familiar with Copilot-specific shortcuts can drastically speed up your coding process.

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8. Domain-specific prompting

Each coding domain or specialty has its unique challenges. You can receive more targeted code solutions by hinting at the specific domain in your prompts.

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9. Handling errors and exceptions

Incorporating proper error handling enhances the robustness of your code. Copilot can provide suggestions, but you should customize them to fit your project’s needs.

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10. Advanced configurations

Tweak Copilot’s settings to fit your development style and project needs, ensuring smoother and faster coding.

• Enable or disable suggestions matching public code. This setting controls whether Copilot will suggest code that it has seen in public repositories. If you are working on a sensitive project, you may want to disable this setting to prevent Copilot from suggesting code that could be malicious or confidential.
• Configure the prompt delay. The prompt delay is the amount of time that Copilot waits before suggesting code. If you are a fast typer, you may want to decrease the prompt delay so that Copilot can keep up with you. If you are a slow typer, you may want to increase the prompt delay so that you have more time to think about the code that Copilot suggests.
• Set the minimum confidence level for suggestions. The minimum confidence level is the level of confidence that Copilot must have in a suggestion before it will be displayed. If you are working on a critical project, you may want to set the minimum confidence level to a high value to ensure that Copilot only suggests code that it is very confident in. If you are working on a less critical project, you may be able to lower the minimum confidence level to speed up the coding process.
• Block or allow specific keywords or patterns. If there are certain keywords or patterns that you never want Copilot to suggest, you can block them. This can be useful if you are working on a project that has a specific style guide or if you want to avoid using certain APIs or libraries.
• Enable or disable the experimental features. Copilot has a number of experimental features that are not yet fully baked. If you are adventurous, you can enable these features to see if they improve your coding experience. However, be aware that these features may not always work as expected.

11. Diving into Integrations

Integrate Copilot with your favorite tools and platforms for an optimized coding experience.

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12. Customizing for language specificity

Although Copilot is versatile, tailoring your prompts for specific languages ensures more accurate code generation.

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13. Harnessing the API

GitHub Copilot’s API provides additional customization and flexibility, enabling developers to integrate AI-powered coding assistance into their custom tools or platforms.

• The API allows developers to control Copilot’s behavior in more detail. For example, developers can specify the programming languages that Copilot should suggest code for, the minimum confidence level for suggestions, and the keywords or patterns that Copilot should block.
• The API also allows developers to integrate Copilot with their own custom tools or platforms. This can be useful for creating custom IDEs, code linters, or continuous integration (CI) pipelines.
• The API is still under development, but it has the potential to revolutionize the way that developers write code. By integrating Copilot with their own tools and platforms, developers can create more efficient and productive coding workflows.

14. Continuous training

Just like any other AI, Copilot improves over time. You make Copilot smarter by constantly using it, providing feedback, and training it on your code.

• Use Copilot as much as possible. The more you use Copilot, the more data it will have to learn from. This will help Copilot to generate better suggestions and to understand your coding style.
• Provide feedback. If you find that Copilot’s suggestions are not helpful or if you see any errors in the code that it generates, you can provide feedback to the Copilot team. This feedback will help the team to improve Copilot’s accuracy and performance.
• Train Copilot on your code. You can also train Copilot on your own code. This will help Copilot to learn your coding style and to generate better suggestions for your specific projects.

15. Experimentation is Key

Always be open to testing out different prompts and approaches with Copilot. Sometimes, the most unexpected queries yield the most effective code solutions.

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16. Community-driven techniques

Tapping into the developer community can open doors to innovative Copilot methods.

• Read forums and discussions. There are a number of forums and discussion boards where developers talk about Copilot. These forums are a great place to learn about Copilot’s features, to get help with Copilot, and to find inspiration for using Copilot in new and innovative ways.
• Attend workshops. There are a number of workshops and meetups that are dedicated to Copilot. These workshops are a great way to learn more about Copilot from the experts, to get hands-on experience with Copilot, and to network with other developers who are using Copilot.
• Contribute to the Copilot community. There are a number of ways to contribute to the Copilot community. You can contribute code, documentation, or feedback. You can also participate in discussions and forums. By contributing to the Copilot community, you can help to make Copilot a better tool for everyone.

17. Updating regularly for new features

GitHub constantly updates Copilot with new features, enhancements, and fixes. Staying updated ensures you’re harnessing its full potential.

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Real-world Copilot programming success stories

Exploring how real-world companies have utilized Copilot can provide insightful lessons.

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Mike Krieger, co-founder of Instagram: Krieger has said that Copilot is “the single most mind-blowing application of machine learning I’ve ever seen.” He has used Copilot to help him with tasks such as writing code for new features on Instagram and debugging existing code.

Potential Copilot programming pitfalls and How to avoid them

Every tool, no matter how advanced, has its downsides. Here’s how to sidestep the most common pitfalls associated with Copilot.

1. Over-reliance on Copilot

Copilot is a powerful tool that can help you with a wide range of tasks, but it is important to remember that it is still a machine learning model. It is not perfect and can sometimes make mistakes. It is important to use your own judgment and expertise to evaluate Copilot’s suggestions and make sure they are correct and appropriate for the task at hand.

2. Not reviewing generated code

Trust but verify. Always ensure that Copilot’s code fits the application context and follows best practices.

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3. Avoiding plagiarism and ensuring originality

While Copilot offers code suggestions, ensuring you’re not inadvertently copying someone else’s proprietary code is imperative.

• Be aware of the code that Copilot is suggesting. Take a close look at the code that Copilot suggests and make sure you understand where it came from. If you are unsure, you can always do a quick Google search to see if the code is proprietary or under copyright.
• Use your own judgment. Don’t just blindly accept Copilot’s suggestions. Use your own judgment to evaluate the code and make sure it is appropriate for your project. If you are unsure, it is always best to err on the side of caution and not use the code.
• Modify the code. If you do decide to use Copilot’s suggestions, modify the code enough so that it is not a direct copy of the original. This will help to avoid copyright infringement.
• Get permission. If you are unsure about whether or not you can use Copilot’s suggestions, you can always get permission from the copyright holder. This is the safest way to avoid copyright infringement.

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Frequently asked questions (FAQs)

When exploring a new tool, questions are bound to arise. Let’s tackle some of the most frequently asked questions about Copilot programming.

Q1. How can I code faster and more efficiently with Copilot?

With Copilot, coding faster isn’t just about accepting suggestions. It’s about understanding them, refining your prompts, and seamlessly integrating AI-generated code with your logic.

Q2. Can GitHub Copilot improve code quality?

Yes, GitHub Copilot has the potential to improve code quality. GitHub Copilot is an AI-powered coding assistant developed by GitHub in collaboration with OpenAI. It is designed to help developers write code faster and with fewer errors by providing intelligent code suggestions and autocompletions.

Here’s how GitHub Copilot can contribute to better code quality:

1. Code Suggestions: Copilot suggests code snippets and completions in real-time as you type. It can help you follow best practices, use appropriate design patterns, and avoid common coding mistakes.
2. Faster Development: With Copilot’s assistance, developers can write code more quickly. This can reduce the likelihood of introducing errors due to rushed coding and increase the time available for testing and refining the codebase.
3. Consistency: Copilot can assist in maintaining coding consistency across a project. It can suggest consistent variable names, formatting, and coding conventions, which can lead to cleaner and more readable code.
4. Error Prevention: By providing suggestions that align with programming language rules and best practices, Copilot can help prevent syntax errors and other common mistakes.
5. Documentation: Copilot can generate comments and documentation for your code, helping to ensure that the purpose and usage of different parts of the code are well-documented. This can contribute to improved code maintainability.
6. Learning Tool: Copilot can introduce developers to new coding techniques, libraries, and design patterns they might not be familiar with. This can lead to the adoption of better coding practices and improved code quality over time.

Q3. How much does Copilot increase productivity?

The extent to which GitHub Copilot increases productivity can vary depending on factors such as the developer’s familiarity with the programming language, the complexity of the project, and how effectively the tool is integrated into the development workflow. Here are some ways in which GitHub Copilot can contribute to increased productivity:

1. Faster Coding: Copilot can provide code suggestions and autocompletions, allowing developers to write code faster. This can be particularly beneficial for routine tasks and repetitive coding patterns.
2. Reduced Search Time: Copilot can assist in finding relevant code examples and solutions to common problems, reducing the time spent searching for documentation or Stack Overflow answers.
3. Learning Efficiency: Copilot can introduce developers to new coding techniques and concepts. Learning on-the-fly while writing code can be more efficient than studying separate tutorials or documentation.
4. Prototyping: Copilot can help generate quick prototypes or mockups by suggesting code for different components or functionalities. This can speed up the initial development phase.
5. Error Reduction: By providing suggestions aligned with best practices, Copilot can help reduce the occurrence of syntax errors and common mistakes, saving time spent debugging.
6. Code Consistency: Copilot can suggest consistent variable names, formatting, and coding styles, leading to better code consistency across the project.
7. Documentation Generation: Copilot can assist in generating comments and documentation for the code, saving time that would otherwise be spent on writing explanations.
8. Complex Algorithms: For complex algorithms or code snippets, Copilot can offer insights and suggestions that expedite the development process.

However, it’s important to note that while GitHub Copilot can enhance productivity, it’s not a silver bullet. Its effectiveness depends on how well it aligns with the developer’s intent and the specific requirements of the project. Developers still need to review and validate the suggestions provided by Copilot to ensure accuracy, security, and adherence to project-specific needs.

Q4. Is there anything better than Copilot?

While Copilot is revolutionary, the tech world is ever-evolving. Other AI-driven coding tools are emerging, but Copilot’s integration with GitHub gives it a unique edge. However, always be on the lookout for new advancements.