Harnessing AI in VS Code, My Journey with AI-Assisted Development

In a world where AI is becoming an indispensable part of coding, this article shares a seasoned programmer’s journey in using AI coding assistants within VS Code. Highlighting both the perks and pitfalls, from efficiency boosts to challenges in context retention, the article explores the growing role of AI in development. Aimed at both experienced developers and those new to coding, it offers a well-rounded view of how AI can enhance, yet not replace, the skill of a human coder.

I’ve been using AI to enhance my coding skills and explore new learning paths for some time now. As an AI coding assistant, it’s been a helpful companion, allowing me to tackle repetitive tasks efficiently and discover alternative approaches to problems. It hasn’t replaced my skills but has expanded them, providing a steady source of insights and creativity in my workflow. This experience has shown that, while AI isn’t a substitute for a developer’s intuition, it’s a valuable tool that’s here to support and augment our abilities.

The AI Habit: Integrating Assistance into the Coding Routine

Having worked with AI coding assistants consistently for over two months, I’ve come to rely on them for certain tasks—almost like a mild habit. The convenience of using AI for repetitive parts of coding, especially for generating tests and building initial code structures, is hard to overlook. AI has become a valuable tool for augmenting my skills, sparking creativity, and introducing new learning opportunities.

However, despite its capabilities, AI isn’t replacing human programmers any time soon. It plays a supportive role: a powerful assistant for routine tasks and a source of inspiration for alternative approaches. Yet, the human coder remains central, responsible for guiding AI and ensuring control over complex, nuanced projects. In 2024, AI serves as an enhancer, not a replacement, and there’s a promising place for tools like this—as long as we use them thoughtfully and strategically.

AI in coding isn’t a replacement but an enhancement—an assistant for the skilled hand, not a substitute for it.

Sections:

• Introduction: Embracing AI in Development
  An overview of the role of AI in modern development and my journey of adapting to AI tools in VS Code.

• Pros of Using AI in Coding: Speed, Creativity, and Learning
  Exploring AI’s benefits, from faster coding to knowledge expansion and creative problem-solving.

• Cons of AI in Development: Losing Context and Control
  Outlining the limitations of AI, such as lack of project-wide awareness and hallucination errors.

• Striking the Balance: When AI Works and When It Doesn’t
  Discussing scenarios where AI performs well and others where it requires a human touch.

• Future Vision: AI’s Role in the Evolution of Coding
  Projecting forward to AI’s potential in enhancing the coding landscape.

Introduction: Embracing AI in Development

As artificial intelligence sweeps across industries, coding is no exception. Tools like AI-assisted plugins in VS Code offer developers the chance to speed up their workflow, tackle repetitive tasks, and even explore unfamiliar languages. Having spent over two months with various AI coding assistants, I’ve witnessed firsthand both the allure and the limitations of these tools. It’s fair to say they’ve changed how I approach certain coding tasks, especially in test generation and handling the more tedious elements of coding.

However, while these AI tools are highly effective, they don’t replace human intuition or deep project understanding. Instead, they act as powerful allies, giving skilled developers more flexibility to focus on complex, creative tasks. This article shares the ups and downs of my journey, offering insights on where AI works best and when it falls short.

Pros of Using AI in Coding: Speed, Creativity, and Learning

One of the most immediate benefits of AI in coding is speed. AI plugins in VS Code, for instance, can generate code skeletons, quickly suggest fixes for common errors, and even brainstorm multiple approaches to a problem—all in seconds. These tools act as a catalyst for getting things done faster, which is especially valuable when facing tight deadlines. With the right prompts, AI efficiently fills gaps that would usually require extensive searches or manual labor.

Beyond mere speed, AI sparks creativity. By suggesting alternative approaches and offering different perspectives, AI has a way of expanding the developer’s view. This comes in handy when learning new languages or tackling problems from angles we might not have initially considered. Additionally, AI opens up access to knowledge without requiring specialized resources, filling gaps and making learning accessible.

AI’s role in routine tasks is another game-changer. By automating repetitive coding parts, such as tests or boilerplate code, AI frees up time for more complex work. It becomes especially helpful when scripting small tools or writing Bash scripts, cutting down on the tedious parts of coding while allowing developers to focus on building the core logic of applications.

Cons of AI in Development: Losing Context and Control

Despite these benefits, using AI in coding isn’t without challenges. One major issue I’ve encountered is AI’s tendency to lose context. Many AI plugins struggle to retain a broad understanding of the project’s goals or structure. This lack of context often results in suggestions that, while seemingly correct, may not align with the project’s overall logic or structure. Additionally, AI tools sometimes propose changes that conflict with other parts of the codebase, causing a ripple effect that can lead to further debugging.

Another frustrating limitation is AI’s tendency to hallucinate. This occurs when AI provides an answer that appears reasonable on the surface but is, in fact, entirely incorrect. It’s not uncommon to see AI propose a solution that, while formatted correctly, has no basis in actual programming logic. This can become especially tedious when the AI gets “stuck” on a particular approach, repeatedly suggesting the same erroneous solution.

AI also has the potential to reduce traffic to knowledge-sharing sites like Stack Overflow, which are valuable for learning through community interaction. Additionally, the tool’s convenience can breed a reliance on AI, which, while helpful, might lead to over-dependence and an unintended weakening of core problem-solving skills. There’s also the risk of imposter syndrome, especially for newcomers, who may feel as though they’re “cheating” by leaning on AI for code.

Striking the Balance: When AI Works and When It Doesn’t

In my experience, AI shines brightest in routine and straightforward tasks—like generating test cases, creating boilerplate code, or drafting prototypes. For these tasks, AI reduces friction and speeds up development significantly. However, in more complex or nuanced coding challenges, where unique solutions are necessary, traditional problem-solving remains the gold standard.

AI often falters when asked to handle project-specific logic or recent technology updates, which it may not recognize due to data limitations. In these cases, a skilled human hand is essential to refine AI’s suggestions and adapt them to the unique needs of the project. By staying actively engaged, developers can harness the strengths of AI while mitigating its weaknesses, ensuring that AI is used effectively as a supporting tool rather than a crutch.

The AI Habit: Developing a Coding Routine with Assistance

After months of working with AI coding assistants, I’ve found that they can be surprisingly addictive. It’s easy to rely on them to handle repetitive parts of coding, especially when it comes to testing. The convenience of AI in these areas means that I find myself increasingly reaching for it to save time. However, while AI is invaluable in this capacity, it hasn’t replaced the need for hands-on coding.

AI tools in their current form are great for augmentation—handling routine tasks, suggesting quick fixes, and assisting with exploratory coding. But as of now, they haven’t crossed the threshold where they can independently manage complex projects. For any substantial project, AI remains a tool to assist skilled programmers rather than a substitute for them.

Future Vision: AI’s Role in the Evolution of Coding

Looking toward the future, it’s easy to imagine a scenario where AI coding tools become even more integrated into the development workflow. Imagine plugins that can adapt to a coder’s unique style, or tools that better understand the overarching goals of a project. As AI continues to evolve, it may eventually bridge some of the context and control issues we see today, perhaps through advanced contextual awareness or project-wide pattern recognition.

But no matter how advanced AI becomes, it’s likely to remain an augmentative force rather than a replacement for human coders. Developers will always bring intuition, creativity, and a deeper understanding of project goals—qualities that AI, at least for now, cannot fully replicate. As AI tools improve, the programming landscape will likely shift, with developers spending less time on repetitive tasks and more on innovation and complex problem-solving.

A New Approach: Structuring AI-Assisted Coding with Clear Documentation

In my current workflow with AI coding assistants, I’ve adopted a more methodical approach that mimics real-world project development. Before diving into any code, I start with brainstorming sessions with the AI—exploring ideas, discussing potential solutions, and examining edge cases for the task at hand. This brainstorming phase allows me to gather a range of perspectives and ideas, laying a strong foundation for the next step.

Once I feel confident in the direction, I channel all of this into a comprehensive documentation markdown file. Here, I detail the project’s objectives, expected outcomes, potential challenges, and specific requirements. I treat this documentation phase as a cornerstone; it’s only when the README is detailed and “project-ready” that I move into actual coding. This structured approach keeps the process organized and ensures that both the AI and I are aligned before any lines of code are written.

This strategy has improved the quality of my projects, aligning AI’s role in my workflow with a realistic, industry-standard approach. Just like a real project, this preparation phase ensures clarity and precision, transforming AI from a quick tool into a robust assistant for structured development.

Conclusion

In conclusion, AI coding assistants in 2024 are powerful tools for boosting productivity and expanding learning but still require skilled programmers to guide and refine their output. While these tools bring speed and convenience to the development process, they also introduce new challenges, such as context loss and hallucination errors. In complex projects, AI is best used as a supplement to human expertise. As we look to the future, the best outcomes will come from a balanced approach—embracing AI where it excels while continuing to rely on the depth of human skill and intuition.