Why Use AI as Your Coding Assistant?

The evolution of software development

In the past, software development was a challenging process. Programmers had to manually input code using punch cards and work on old computers that could only do one thing at a time.

The code itself was also manual, step-by-step, and often repetitive, requiring a lot of effort even for simple programs. As programming languages evolved to use objects and reusable parts, development became faster, but it was still limited.

Today, things are very different. AI and machine learning tools are changing the software development landscape. What used to be tedious and labor-intensive is now more creative and efficient. Developers can now focus on innovation rather than getting bogged down in the hard work of coding. AI assistants can handle many of the repetitive coding tasks, freeing up developers to be more creative and come up with new ideas. This boosts their overall productivity and smooths the whole coding process.

Future-proofing your programming career

In today’s technology world, it’s really important to start using AI tools.

According to Communications of the ACM"The Impact of AI on Computer Science Education," Communications of the ACM, July 2024, https://cacm.acm.org/news/the-impact-of-ai-on-computer-science-education/, AI is reshaping the workforce by automating tasks, but it does not eliminate jobs; instead, it is transforming the types of tasks that workers will perform. AI may replace certain aspects of a job, but the overall role and need for human expertise will remain.

Making use of AI coding assistants

AI-powered coding assistants like ChatGPT, GitHub Copilot, and Codeium are transforming software development. These tools can do all sorts of helpful things for developers.

They can write code for you, give you tips on fixing problems in your code, and even generate documentation. Using such AI assistants streamline the coding process, making it faster and more efficient.

Common misconceptions about AI in coding

There are some common misconceptions about AI and its role in tech, particularly in coding. Let’s look at some of these misconceptions:

Reality: While AI can assist with code generation, debugging, and pattern recognition, it still requires human insight to create meaningful, well-structured, and contextually appropriate solutions. AI is a helpful tool, but it can’t do everything a human developer can. AI lacks the creativity and problem-solving skills of people. AI supports developers, it doesn’t replace them.

Reality: AI tools like ChatGPT and GitHub Copilot are designed for developers of all skill levels, from beginners to experts. They can be helpful for everyone.

Reality: AI can streamline certain coding tasks, but it doesn’t eliminate the need for core coding skills and problem-solving ability. Those fundamental skills are still essential.

Reality: AI is a rapidly advancing field with increasing applications. Learning to use AI tools is an investment in skills that will be valuable in the long term.

Reality: AI can help generate code, but it’s not perfect. The code still needs to be reviewed and adjusted by human developers to ensure it’s accurate and high-quality.

Essential skills for AI-augmented development

Let’s look at some of the skills needed to leverage AI effectively.

1. Strong programming fundamentals: With the rise of such AI-powered coding assistants, some may question the necessity of learning to code. However, experts argue that while these tools are helpful for programming tasks, understanding the fundamentals of coding remains essential for computer scientists. You still need a solid understanding of basic coding concepts and techniques. This helps you properly use and interpret the suggestions made by AI assistants.

Reid Gordon SimmonsReid Gordon Simmons, "The Impact of AI on Computer Science Education," Communications of the ACM, October 2024, https://cacm.acm.org/news/the-impact-of-ai-on-computer-science-education/., a research professor at the Robotics Institute at Carnegie Mellon University, drew an analogy to emphasize how learning to code is still vital for understanding processes and systems: “My analogy is teaching seven-year-olds arithmetic. By the time you get to middle school, no one does arithmetic by hand; you use calculators.”—Simmons.

2. Problem-solving and critical thinking: AI can generate code for you, but you still need to know how to evaluate, refine, and troubleshoot that code to ensure it works well. Your problem-solving skills are still crucial. When solving a problem, it’s vital to learn to break the problem into manageable chunks. Breaking a problem into smaller parts lets us use AI to tackle individual aspects instead of the entire project.

As Eric KlopferEric Klopfer, "The Impact of AI on Computer Science Education," Communications of the ACM, July 2024, https://cacm.acm.org/news/the-impact-of-ai-on-computer-science-education/., professor and director of the Scheller Teacher Education Program and The Education Arcade at MIT, puts it, “These skills, of how to break down the problem, are critical to learn.”.—Eric Klopfer.

2. Generative AI prompting basics: Learning how to effectively “ask” AI tools like ChatGPT for help will get you better, more relevant code suggestions. This makes using the AI a smoother, more productive part of your workflow.

3. Adaptability and openness to evolving tools: AI tools for coding are constantly improving and changing. Being flexible and willing to learn new AI assistants will help you stay up-to-date and take full advantage of the latest AI capabilities in your work.