After twelve years building full-stack applications, I’ve spent the last two years integrating AI tools into my daily workflow. The discourse around AI in software development tends toward extremes, but the reality is more nuanced. AI has meaningfully changed how I work, though not in the ways the hype suggests.
Code Generation for Standard Patterns
AI excels at generating boilerplate code for common patterns. When I need Express routes, database models, or API endpoints that follow established conventions, tools like GitHub Copilot and Claude can produce working implementations in seconds. The output typically requires review and adjustment to match project conventions, but it eliminates the mechanical work of typing out familiar structures.
For migrations, schema definitions, and CRUD operations, the time savings are substantial. The key is recognizing that AI-generated code serves as a starting point, not a final implementation.
Debugging and Error Analysis
When encountering unfamiliar error messages, particularly from third-party libraries, AI can provide useful context. I often paste stack traces into Claude to understand what might be causing an issue. The explanations help narrow down the problem space, though I always verify the suggested solutions against the actual codebase.
This is particularly valuable when working with libraries outside my core expertise. The AI provides a faster initial direction than searching through documentation or Stack Overflow, though it cannot replace understanding the underlying systems.
Limitations in Architecture and Security
AI tools cannot make architectural decisions. They will generate code for whatever pattern you describe, regardless of whether that pattern is appropriate for your scale, performance requirements, or maintainability needs. Database schema design, API contract decisions, and system architecture still require human judgment.
Security is another area where AI assistance requires careful oversight. I have reviewed AI-generated code containing SQL injection vulnerabilities, missing authentication checks, and insecure session handling. The tools know common patterns but do not reason about threat models or defense in depth strategies.
My Integration Approach
I design the architecture and key interfaces myself. AI assists with implementation of standard patterns and boilerplate. I review every line of generated code before committing it. Tests are typically written manually, as AI-generated tests often focus on coverage metrics rather than meaningful behavior verification.
This approach preserves the time savings from AI assistance while maintaining the quality and security standards production code requires. The value comes from eliminating repetitive typing, not from eliminating the need to understand what the code does.
Where This Technology Fits
AI tools are valuable force multipliers for experienced developers. They accelerate implementation of patterns you already understand. For developers still building foundational knowledge, AI-generated code can be difficult to verify and debug.
The developers who benefit most from these tools are those who can read generated code and immediately identify what is correct, what needs adjustment, and what represents a fundamental misunderstanding of the requirements. That skill comes from experience, not from prompt engineering.
I use AI daily. It has made me more efficient. But the efficiency comes from better tooling for work I already knew how to do, not from the ability to build systems I could not build before.