How to Use AI Coding Assistants Without Losing Your Problem-Solving Skills
A growing number of developers are grappling with an unexpected side effect of using AI coding assistants: a feeling that their own problem-solving skills are beginning to dull. While tools like Copilot significantly boost productivity, their constant, predictive nature can hijack a developer's thought process, leading to a more passive and less engaged approach to coding. This has sparked a conversation about how to best leverage these powerful tools without sacrificing critical thinking and mental acuity.
The Core Dilemma: Efficiency vs. Independent Thought
The central tension lies between the professional demand for efficiency and the personal desire to maintain and grow one's skills. As one developer noted, their tolerance for how long a problem should take has decreased, creating a difficult balance. The temptation to reach for an LLM for any non-trivial challenge is strong, especially when the primary goal is to deliver results quickly. However, this convenience comes at the cost of the deep thinking and learning that occurs when wrestling with a complex problem independently.
A Shift in Mindset: Treating AI as a Partner, Not an Oracle
One of the most productive approaches suggested is to reframe the relationship with the AI assistant. Instead of blindly accepting its suggestions, developers should treat it as a junior programmer they are pair-programming with.
This involves:
- Maintaining Skepticism: Actively question the AI's output. Does it follow best practices? Is it the most efficient solution? Are there edge cases it missed?
- Critically Reviewing Code: Read through every line the AI suggests. The responsibility for the final code still rests with the developer, and a thorough review is non-negotiable.
- Using it for Ideation: Leverage the AI to bounce ideas around and explore different approaches, rather than simply asking it to produce a final solution.
By adopting this mindset, the developer remains the driver of the problem-solving process, using the AI as a powerful but subordinate tool.
Alternative Tools for a More Deliberate Workflow
For some, the issue lies with the "always-on" autocomplete model of tools like Copilot. An alternative approach gaining traction is found in assistants like Claude Code, which offer a more structured and interactive workflow. Instead of autocompleting as you type, these tools require the developer to issue a command. The AI then analyzes the codebase and proposes a detailed plan of action. The developer can review this plan, and if approved, the tool walks them through the proposed changes one by one, allowing for approval at each step. This step-by-step process keeps the developer firmly in control and ensures they understand the why behind each change, fostering a more deliberate and engaged coding experience.
The Pragmatic View: Embracing the Evolution of Development
Finally, a strong counter-argument posits that this is a natural evolution of software development. Just as developers moved from assembly to compilers or from manual memory management to garbage collection, AI assistants are the next step in abstracting away "undifferentiated heavy lifting." From this perspective, the goal is to deliver business value, whether that's by making money or saving it. If an LLM can write boilerplate code or solve a routine problem faster, it frees up the developer to focus on higher-level architecture, business logic, and more complex challenges. As one commenter put it, "this isn't college... you don't get points taken off if you don't show your work."