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How To Use AI To Write Code & 9 Leading Platforms to Use

Are you a coder looking to boost your productivity and create more powerful software? Discover how to use AI to write code with the help of AI Copilots. AI Copilots are transforming the coding process by accelerating development and aiding coders with automated suggestions and corrections. Explore how AI Copilots can enhance your coding experience and help you create better software faster. Dive in to learn more about how to leverage AI in coding and take your projects to the next level.


What Is AI-Generated Code?

When it comes to AI-generated code, understanding how artificial intelligence technologies generate or suggest code snippets automatically is the key. What happens behind the scenes is that machine learning models are trained on large datasets of code to grasp and generate programming syntax. This technology is capable of handling various types of coding tasks, ranging from simple syntax generation to more complex code solutions.


AI in the realm of Coding

The question of when Artificial Intelligence (AI) will be sophisticated enough to write its own code, resulting in self-proliferation of AI’s is still a matter for science fiction writers. Nonetheless, recent advances in Machine Learning (ML) have made it feasible to automate the writing of code snippets, or even help assist programmers by automating the writing of “whole lines or entire functions right inside your editor,” which is the promise of GitHub’s Copilot. 


The saying, “a good programmer is a lazy programmer,” is perhaps perfectly epitomized in using code to automate the writing of code. This ability to automate code snippet writing or even generate entire functions right inside your editor is beneficial for developers who can save time and focus on solving more complex problems.


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Advantages of Using AI to Write Code

The primary benefit of AI code generation is the ability to write code in less time, as routine coding tasks and writing tests are handled by AI, leaving developers free to focus on work that involves critical thinking and problem-solving.


In a survey of more than 2,000 developers, those who reported the highest productivity gains from using GitHub Copilot were also the ones who had accepted the largest number of code suggestions.GitHub research shows that developers using GitHub Copilot reported being able to focus on more satisfying work, which increased productivity and allowed them to complete repetitive tasks faster. This led to higher developer satisfaction and overall productivity.

Enhancements AI Brings to Coding and Software Development

Another benefit of AI code generation is the use of testing tools that catch and provide suggestions to fix bugs before deployment. These tools examine the code structure and recommend security enhancements, ensuring that the code is robust and secure from the start.


AI-assisted coding tools empower non-developers within the team, such as product managers or designers, to understand and contribute to the codebase. These tools lower the learning curve for those new to coding, enabling novice coders to get code suggestions and textual guidance based on existing code examples and best practices. By encouraging non-developers to learn about code quickly and intuitively, AI coding tools can spur better cross-functional collaboration within software development teams.

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How to Use AI to Write Code

To start incorporating AI into your coding practice, the first step is to familiarize yourself with AI code generation tools like GitHub Copilot. These tools are designed to assist developers by offering code completions and suggestions based on their inputs. GitHub Copilot, for instance, provides contextualized coding assistance across multiple programming languages, making it a versatile tool for developers of all experience levels.


Using GitHub Copilot to Autocomplete Code

One of the key features of AI-powered code generation tools like GitHub Copilot is autocomplete functionality. This function allows developers to receive code suggestions while typing, which can significantly speed up the coding process. Whether you are a beginner or an experienced developer, autocomplete suggestions can be invaluable in helping you write code more efficiently.


Leveraging Explicit Code Comments for Better Suggestions

By using explicit code comments within your code, you can guide AI coding tools like GitHub Copilot to provide more accurate and relevant suggestions. These comments can help the AI understand your intent and assist you in generating the code you need, especially when working in unfamiliar environments or with new libraries. Naming functions and variables descriptively and including documentation can enhance the AI's ability to offer useful suggestions.


Engaging with AI through Chat

Engaging with AI through chat functionality can make code generation more interactive and collaborative. Tools like GitHub Copilot Chat allow developers to ask for explanations, improve syntax, generate new ideas, create tests, and modify existing code. By fostering a dialogue with the AI, developers can refine their coding tasks and receive more comprehensive assistance.


Enhancing Collaboration with AI in Coding Tasks

Using AI to write code is not about replacing human developers; it's about enhancing their capabilities. By working closely with AI tools like GitHub Copilot, developers can improve their coding efficiency, learn new programming languages, and maintain cleaner and more maintainable code. Ultimately, the symbiotic relationship between developers and AI can benefit entire development teams by streamlining coding tasks and fostering collaboration.


9 Leading AI Platforms That Write Code

1. Omnipilot: AI Copilot for MacOS

Omnipilot is an AI tool that offers a MacOS app, designed to provide coding assistance, including typing, context awareness, and overall automation. This tool supports coding across different environments on MacOS, such as in the terminal, emails, and team updates. It offers a seamless integration with MacOS, allowing users to have an assistant for coding and writing tasks.


2. CodeBots: Multilingual Code Writing Tool

CodeBots is a platform aimed at training automated bots to create code in various programming languages, including Java, Python, and JavaScript. It provides a unique approach to code generation by allowing users to tailor bots according to their specific requirements. This feature adds flexibility and customization to the coding process.


3. Codota: Code Completion and Search Engine

Codota is a website that offers code completion suggestions based on a user’s coding habits. It also provides a search engine for finding code snippets and examples. This tool enhances coding efficiency by offering predictive suggestions, thus facilitating the coding process for developers.


4. DeepCode: Collaborative Code Analysis

DeepCode is a platform that uses machine learning to review and enhance code quality. It also has a range of collaboration and code review features. By employing machine learning techniques, DeepCode assists developers in identifying bugs or issues in their code, thus promoting code quality and efficiency.


5. GitHub Copilot: Code Completion Tool

GitHub Copilot, a joint project by GitHub and OpenAI, is a code completion tool integrated into various code editors such as Visual Studio Code and Neovim. This tool employs few-shot learning to provide context-based code suggestions, which can significantly enhance coding productivity and quality.


6. TabNine: AI Code Assistant for Editors

TabNine is an AI assistant tailored for editors like Vim, Emacs, and Sublime Text, focusing on fast code completions. It provides a quick and efficient code suggestion mechanism, which can boost a developer’s coding speed and output.


7. Phind: Search-Enabled Coding Assistant

Phind is an AI assistant that aids in debugging, locating documentation, and writing code. It facilitates codebase searches and allows users to ask coding-related questions, enhancing the efficiency and quality of coding processes.


8. Continue: ChatGPT Integration in VS Code and JetBrains

Continue is an IDE extension that incorporates ChatGPT capabilities into Visual Studio Code and JetBrains IDEs. This tool enables editing, generating, and understanding code through natural language instructions, making coding more intuitive and accessible to developers.


9. Cody: AI Coding Assistant

Cody leverages a user’s codebase to provide accurate coding completions and chat-based answers. It is compatible with various programming languages and Integrated Development Environments (IDEs), enhancing coding efficiency and accuracy through AI assistance.


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6 Best Practices for Using AI to Write Code

1. Providing Initial Context

When using AI tools for coding, it is essential to provide initial context to help the AI generate more relevant recommendations. This could include writing import statements, defining key variables and functions, or adding comments about the project's goal. This context assists the AI in tailoring its suggestions to your specific project.


2. Letting It Learn From Your Edits

To enhance the effectiveness of AI tools for coding, it is crucial not to accept suggestions blindly. By modifying the AI-generated code to match your desired style and conventions, the AI assistant learns from your real-time edits, selections, and deletions. Over time, it adapts to produce code that better aligns with your preferences.


3. Focusing on Intent Over Details

Avoid getting bogged down by minor stylistic tweaks to the AI's suggestions and instead concentrate on evaluating and approving code that captures your overall intent. The key is to efficiently move your logic forward rather than getting stuck on smaller refinements.


4. Leveraging Code Reviews

Even code generated with AI assistance should undergo human review. Having a peer manually inspect key portions for bugs, anti-patterns, or other issues the AI may have missed is essential. Two sets of eyes are always better than one in ensuring code quality.


5. Complementing Your Skills

While an AI assistant is excellent at producing boilerplate code and handling repetitive tasks, it is important to rely on your human judgment for higher-level architecture, complex logic, and creative algorithms. Maximizing these complementary strengths results in better code quality.


6. Giving It Time To Learn

Similar to onboarding a new team member, an AI coding assistant requires time to ramp up. Patience is key in allowing it to train on your codebase and gain exposure to your specialized domains. Over time, the AI will become increasingly useful and efficient in generating code according to your preferences.


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Try Our MacOS AI Copilot for Free Today

Omnipilot is a revolutionary tool that harnesses the power of artificial intelligence to enhance the coding experience for developers. This AI-driven solution is designed to assist coders in writing code more efficiently and accurately by providing real-time suggestions and auto-completion. Omnipilot can transform the way developers interact with their code editor by providing intelligent recommendations tailored to the specific context of the task at hand.


Enhancing Coding Efficiency with Omnipilot's AI Copilot

Imagine having an AI-powered copilot that can effortlessly type anywhere on macOS, complementing your existing coding skills. With Omnipilot, writing code becomes a seamless and intuitive process, regardless of the complexity of the task at hand. This AI copilot integrates seamlessly with your existing workflows, saving you time and effort in writing clean, error-free code.


Expanding Omnipilot's Capabilities Beyond Coding

The potential applications of this technology are vast, extending beyond the realm of coding to various other tasks that involve text input on macOS. Whether you need to compose an email, write a script, or update your team on a project's progress, Omnipilot can be a valuable ally in boosting your productivity.