The power of AI in coding: Enhancing efficiency and innovation

Gone are the days when artificial intelligence was only something the Jetsons and other futuristic fictional families used. AI is embedded in many activities today, from streaming television entertainment to finding products you want to buy online. AI is even prevalent in coding, as it can help automate tedious processes and provide human developers assistance when they tackle tricky troubleshooting problems.

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Whether they're working on marketing integration tools or creating customer-facing software applications, developers use AI for coding. In fact, 92% of coders in the U.S. were using AI tools as of 2023, and 70% said those tools made them better at their work (GitHub). Keep reading to explore the benefits and possibilities of AI for coding.

Is it possible to use AI for coding?

Absolutely! Artificial intelligence has come a long way in recent years, and advancements in machine learning, natural language processing, and other technologies have enhanced the use of AI for numerous tasks. People are finding new ways to use AI in social media marketing and content marketing, financial management, and—yes—in coding.

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AI algorithms are advanced enough now to analyze and understand code patterns. This helps computers generate relevant and functional code for a variety of common scenarios. Machine learning capabilities help companies and coders continue to train AI to assist in increasingly complex coding tasks, including debugging programs.

How can AI help with coding?

As with any use of artificial intelligence, AI for coding isn’t a replacement for human problem-solving and coding. Instead, AI is a tool that can be leveraged by knowledgeable coders to make work easier, more efficient, and higher quality. Some ways developers use AI today include:

• Automating repetitive tasks. Programming often includes writing the same or similar strings of code repeatedly. Programmers could create a store of much-used code and use keyboard shortcuts or macros to call that code when needed. But they can also use AI to automate these repeat tasks, streamlining development processes.
• Offering intelligent coding suggestions. Natural language processing and machine learning helps AI understand the concept and goal of a program and offer intelligent suggestions for completing or improving it.
• Auto-completion of coding lines. AI solutions can complete common phrases as you type—think about the use of autocorrect and auto suggestions when you text. These same capabilities help coders work faster.
• Bug detection and troubleshooting assistance. Even non-coders know the joke about having to find a missing semicolon in thousands of lines of code. AI substantially speeds up the process of debugging troublesome code by locating potential issues.

Benefits of using AI for coding

Using AI for coding provides a number of potential advantages, including:

• Increased productivity and efficiency. Automating repeat tasks and using AI to quickly find issues with code can remove hours of tedium from a developer's workload. This increases productivity and helps reduce critical metrics such as time to market.
• Enhanced code quality. AI troubleshooting increases the overall quality of code in the same way grammar, spellcheckers, and other automated writing tools help increase the quality of a document.
• Fosters creativity and innovation. When developers aren’t bogged down in the nitty-gritty or spending time on repeat tedium, they are able to focus on creative solutions and new innovations.

How to use AI for coding

The benefits of AI for coding aren't automatic. Following best practices, such as those discussed below, help you get the most out of artificial intelligence solutions for any type of work.

Select the right AI tools for your projects (670)

This is a top tip for any use of AI, whether you want to automate social media marketing or speed up coding processes. Hundreds of AI tools now exist, and technology vendors develop more every day.

Some of these tools are designed for very specific tasks—such as finding unclosed parenthesis or other punctuation issues causing problems with code. Other AI tools provide a more comprehensive approach, offering a sort of digital assistance to coders as they work. Take time to research your options and select the tools most likely to provide help in areas you need it.

Don't set and forget AI coding tools

A common mistake content marketers make when using AI to generate blog posts or articles is forgetting to humanize AI content. Computers can do amazing things, but they aren't humans. It's important to remember that—as technical as it often is—coding also requires human creativity and thinking.

Avoid maximizing the use of AI tools to the point that you remove the human element. Doing so isn't productive, as it can create a circular effect that leads to lackluster coding and a lack of innovation. AI is also not equipped to fully understand the complex and intricate nature of business or customer needs and translate those things into effective programming on its own.

Invest in continuous learning

Treat AI as you do other technical and coding tools, including investing in learning and training about these solutions. This can include reading AI coding news regularly, attending webinars on the topic, or setting aside a bit of time every week to practice using AI tools in coding.

Be transparent about your use of AI

You don't need to give away all your coding secrets to clients or others, but it is important to be open about how you use tools to create programs. This can be especially true when you're working with a team of coders or other technical employees, as misconceptions about AI still make people fearful that it will replace them.

Ensure teams understand that artificial intelligence is a tool meant to help them succeed with their work and not a magic coder that will replace them all.

AI tools for coding to look out for

If you want to start practicing AI for coding today, consider some of the tools below. They represent a tip of the proverbial iceberg when it comes to AI tools for coding.

• CodeRabbit: This ai-code reviewer automates a line-by-line review of code, providing in-code suggestions to help programmers make corrections or optimize code for specific purposes. This solution can help coders develop more efficient self-review processes or enhance QA tasks for programming teams.
• Cody from Sourcegraph: Cody is an AI tool that autocompletes common lines of code, reducing how much programmers must manually type. It streamlines coding without the need for tedious copy/paste workarounds and helps reduce errors that come from typos.
• AlphaCode from DeepMind: AlphaCode lets coders copy in large amounts of code and receive feedback about the efficacy and quality of the code. This is another tool that is useful in QA and debugging processes, but it can also be used by coders to teach themselves more about their coding language and how to best use it.
• Amazon CodeWhisperer: This AI coding tool from Amazon leverages machine learning based on billions of lines of existing code. It uses that knowledge to construct code snippets and functions based on instructions from programmers, which might include partial code and natural language descriptions of how the program should work.
• MutableAI: This tool automates the creation of a coding wiki, making it easy for organizations to create extensive knowledge bases to support programming teams. Automations include options to create citations and code diagrams, and the solution makes updates to documentation as source code changes.
• ChatGPT: ChatGPT isn't designed specifically to help with code, and it can handle a lot of other content creation, administrative, and informational tasks. Programming teams might use it to generate simple snippets of code automatically, or for research or brainstorming tasks. For example, you might ask ChatGPT where to find information about a specific coding challenge or request that it provide a list of approaches to a programming need.
• Tabnine: This AI coding assistant can automatically create snippets of functional code based on natural language input from programmers. For example, you can tell Tabnine you want to insert code that fetches the current temperature in a city and displays it to the user, and the system will begin writing code for that function. It also autocompletes as you type, making manual coding tasks faster.
• Codiga: Codiga applies rules from the Codiga Hub to help teams analyze, fix, and create code. This solution works in a variety of environments, making it a flexible option for teams that want to bring automation into their existing coding infrastructure.

Whether you want to white label AI software to increase the services your agency can offer, leverage AI for digital marketing, or code with AI, there are many tools you can leverage. Always do your research and test tools to ensure they work with your other processes—and remember that artificial intelligence can enhance efficiency and reduce costs, but it can't 100% replace the human element.