As a Senior Backend Engineer, I've integrated AI tools into my daily workflow to explore their potential benefits. While these tools offer some promising capabilities, my experience has been mixed, particularly in how they align with professional development practices and coding standards.
AI-powered code suggestions in Integrated Development Environments (IDEs) can be a double-edged sword. While they frequently provide irrelevant or unhelpful recommendations, there are times when the AI understands the context and offers relevant suggestions. In certain cases, it's like having an auto-complete feature that can finish the line of code you're writing. This is especially useful in small, focused tasks, such as suggesting a method call or a quick syntax fix. However, the scope of usefulness remains narrow. Once the task involves broader architectural decisions or complex design patterns, the suggestions tend to lose relevance and can become more distracting than helpful.
AI tools sometimes generate code that works but doesn’t consider the architectural principles or design patterns we adhere to. This can lead to solutions that are technically correct but don’t fit well within the overall system design, requiring additional time to refactor or rework the code to fit our standards.
A significant risk arises when developers lacks a clear idea of what they need. In such cases, AI-generated suggestions can be as dangerous as blindly copying and pasting from documentation. Without a strong understanding of the context, architecture, and design patterns, the resulting code may function but lack the necessary quality or coherence within the project.
One area where AI has proven particularly useful is in generating pull request summaries on GitHub. AI can efficiently identify the key updates, goals of the pull request, and even highlight refactoring efforts and bug fixes. This helps reviewers quickly grasp the scope of changes, making the review process more streamlined and focused.
AI companions can also help describe the behaviour of methods or classes, especially when dealing with large or complex codebases. As developers, we often face the challenge of understanding intricate methods or sprawling classes. AI can assist by providing a quick summary of what a particular section of code does in a matter of seconds. While this doesn’t replace an in-depth personal analysis, it helps rough out the work and quickly identify areas that may need more detailed attention.
One of the most effective ways I’ve used AI is as a conversational companion. Engaging in a dialogue with AI about specific technical points allows me to explore different approaches and refine solutions. This collaborative process feels similar to discussing issues with a colleague, where AI assists in thinking through complex problems.
Additionally, AI can be seen as an interactive form of documentation. One strong advantage is its ability to integrate public documentation and answer related questions. While we must always maintain a critical mindset regarding AI-generated responses, the more precise the question, the more relevant the AI's answer tends to be—much like a conversation with a knowledgeable person. This makes it particularly useful for developers needing quick references to documentation, saving time while still allowing for deeper analysis later if needed.
Another area where AI proves helpful is in writing and editing documents. By providing a prompt with specific instructions—whether to be concise or more verbose, or to adjust the tone of the message—AI can help reformulate certain ideas in a clearer, more structured way. It can maintain the core meaning while improving the clarity of expression. This becomes especially useful when drafting technical content or communicating complex topics, making the writing process faster and more efficient.
In summary, while AI tools offer potential, they often fall short when it comes to the nuanced requirements of senior-level development work. However, they can be valuable when used for tasks like generating pull request summaries, providing quick code descriptions, answering documentation-related questions, and assisting in the writing process. For now, using AI as a discussion partner and an aid in communicating ideas has proven to be the most productive approach in my experience.
This is my feedback right now, but as AI tools improve rapidly, this could evolve quickly. In fact, AI has already evolved since I started writing this article. I expect my feedback to be very different in six months, and that's a good thing. My position on AI is not to ignore it, nor to be afraid of it, but to learn how to work with it while always keeping a critical mindset. The question I continually ask is, "How can it help me be more efficient?" So, my focus is on finding ways to use AI where it is relevant to my job and aligned with my personal workflow.
Thank you for taking the time to read this article. Feel free to share your thoughts, experiences, or questions in the comments. I'm interested in your feedback, and it can help enrich the discussion around the use of AI tools in development.
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