How beginner and expert coders use AI coding tools to code faster and ship great software.

What are AI coding tools?

AI coding tools, or AI coding assistants, help developers write and improve code using large language models (LLM), natural language processing (NLP), and other forms of generative AI. These tools assist coders of all skillsets by automatically generating, reviewing, and optimizing code. They can be used on their own, or they can be added to your integrated development environment (IDE) for real-time assistance.

To start with a coding assistant, you have the option of either writing your own code, which will drive the tool to generate code completions as you go, or you can directly chat with the assistant by opening up an interface and writing a prompt that describes the business logic you want—and what you’d like it to do. The tool then uses AI to analyze the context and generate coding suggestions.

Let’s explore how both beginner and expert developers use AI code completion tools and coding assistants to grow their skillset, boost productivity, and ship quality, next-gen software.

How AI coding tools work

AI coding tools exist thanks to the rise of large language models (LLMs), a large-scale neural network and form of machine learning that uses deep learning algorithms to read, process, and predict language outcomes from large sets of data. Some of today’s most exciting, cutting-edge tools use LLMs to improve the quality of their AI so that they can be put to everyday use.

LLMs work in tandem with natural language processing (NLP), a type of AI that’s focused on developing a computer’s ability to understand and replicate human text and speech. NLP gives computers the ability to automatically classify the grammatical, semantic, and emotional elements of any given piece of text, and to use that information to form natural language in return. This is the technology that drives mapping services, dictation tools, chatbots, and other modern-day, language-based applications.

When paired with LLMs, NLP systems gain the ability to learn from data, to hone their behavioral patterns to reflect these findings, and to translate natural language into code. This is accomplished by pretraining the language model on large volumes of open source code from a network of public repositories. As it runs, the LLM classifies data, extracts meaning, and can even be trained to learn style conventions for a more personalized touch. AI-powered coding tools bring many exciting benefits to software development, but they also present compelling new questions around matters of accuracy, security, and ethics. In terms of accuracy, AI coding tools are designed to generate the best possible code for the given context, but the quality of that code is largely dependent on the size and diversity of the training data, as well as how much context the coding tool is able to take in. And because these tools are trained on public code, there’s always the risk that they could end up learning from inaccurate, insecure, or outdated code.

This is why AI-generated code still needs to be carefully tested and reviewed by humans before it’s shipped. In the end, a developer should still have the final say.

How AI coding tools benefit beginners

For beginners who are new to coding or just looking to grow their skillset, AI coding tools can help you learn foundational concepts faster. Here’s how:

• Code completion. Beginner coders may still need to familiarize themselves with the syntax and logic of their chosen language. AI coding tools generate suggestions for code completion, providing a more streamlined learning experience to students and novices alike.
• Error detection. Junior developers often learn by finding bugs in the code, then fixing them. AI coding tools can identify and flag potential errors, as well as provide suggestions for remediation. Not only does this save time, but it also reduces the likelihood of introducing newer errors to the codebase.
• Built-in guidance and support. AI coding tools can provide inline code snippets and documentation while a developer is coding, allowing them to try out new solutions and stay in flow for longer—no more switching from window to window.

How AI coding tools benefit experts

An AI coding tool can act as a powerful learning tool for beginners, but it also has the potential to bring significant benefits to even the most seasoned developers. Here are just a few ways AI coding tools help expert developers in their day-to-day:

• Automated tests. By automating the tedious, time-consuming process of writing and running unit tests, AI coding tools free up time, energy, and resources among developers to have more fun with their code. Now, experts can focus their efforts on building more satisfying, high-priority projects—and ultimately, better software.
• Code explanations. When developers are required to work off of someone else’s code, AI-powered explanations help them get to speed faster by providing an interpretation of what the code is doing. Experts may now spend less time onboarding and troubleshooting, and more time getting things done.
• Well-commented code. Developers use comments in the source code to describe updates, flag bug fixes, and track changes. When prompted, AI coding tools can generate comments, code snippets, and other forms of documentation as you code, leading to clearer communication and more efficient collaboration across the entire organization.

Conclusion

AI coding tools help beginners by suggesting code completions, finding bugs, and providing inline documentation. They also help experts stay in flow for longer, cutting down on development time while ramping up productivity.

Still, despite these impressive feats, AI coding tools aren’t meant to replace the security practices and processes that are currently in place today. As the developer, you’re ultimately responsible for validating the quality of your code, which is why every developer should continue to evaluate AI-powered code through comprehensive testing, IP scanning, and security checks.

Start exploring the many benefits of coding tools, coding assistants, and generative AI with GitHub Copilot. Built using an assortment of LLMs, including the OpenAI GPT 3.5 models, GitHub Copilot draws context from comments and code to generate coding suggestions in real time, right from your editor. By turning natural language prompts into code across dozens of languages, GitHub Copilot enables developers to code faster and focus on business logic over boilerplate.