AI in web development

Like a vast majority of developers, I use artificial intelligence on a daily basis. It has become a habit. With tools like GitHub Copilot, Cursor, or Claude Code, we no longer just generate a few lines of code: AI is involved across the entire lifecycle of an application. Let’s take a look.

Designing faster

Before even writing a single line of code, AI has become an excellent partner for thinking about application architecture. It helps compare different approaches, propose project structures, identify the most suitable technologies, and even generate diagrams and technical specifications.

One of its main strengths is that it can be challenged from multiple angles. I regularly ask it to defend an architecture, propose an alternative, identify weaknesses in a technical choice, or act as a senior architect to highlight design flaws. This often leads to more relevant architectural decisions. However, AI can sometimes be a bit too agreeable.

Accelerating development

Code generation remains the most well-known use case. AI can create full components, generate REST APIs, produce SQL migrations, convert algorithms from one language to another, or even write particularly complex regular expressions.

The real value lies in context: modern assistants analyze large parts of the project to respect naming conventions, existing patterns, and dependencies. As a result, we spend less time writing repetitive code and more time solving real business problems (at least for now, I still feel clearly superior to AI in that area 🙂 ).

Debugging more efficiently

Debugging is probably one of the areas where AI provides the most value in my experience.

When a unit test fails, you can ask the agent to analyze the console output and iterate to identify and fix the issue.

AI is also extremely useful for analyzing log dumps, explaining complex exceptions, or quickly understanding legacy projects. What used to take tens of minutes to track down can often be identified in just a few exchanges.

Generating tests and documentation

Writing tests is not my favorite part as a developer (even though I sometimes find it quite relaxing as a task), yet they are essential.

From an existing class, AI can generate unit tests, suggest edge cases that were overlooked, or propose integration scenarios. It can quickly produce an initial version of the tests, which then only need to be refined or adapted.

It is just as effective for producing technical documentation. In a few seconds, it can generate a README, document an API, explain a complex class, or write the XML, Javadoc, or PHPDoc comments required in a project.

Optimizing performance

Once the application is built, AI can act as a code review tool.

It detects duplications, suggests refactorings, recommends algorithmic optimizations, identifies inefficient SQL queries, or highlights common security issues.

Of course, not all recommendations are relevant. They must be reviewed with a critical mindset, but they often provide a solid starting point for improvements.

Ticket management and support automation

Another major contribution of AI in development concerns ticket management and technical support. Beyond one-off bug analysis, it is now possible to automate part of the diagnostic process using skills or custom agents. For example, you can build a skill that automatically retrieves application logs, identifies recurring errors, and links them to an existing ticket in tools like Jira or GitHub Issues.

AI can then analyze these logs, suggest a root cause hypothesis, and even propose a priority level or responsible team. In some cases, it can enrich the ticket with relevant technical information (stack traces, runtime context, deployment version). This significantly reduces the time spent qualifying issues and improves the responsiveness of both development and support teams.

In the long run, these automations turn incident management into a much more structured workflow, where developers focus on validation and resolution, while AI handles data collection and initial analysis.

Conclusion

AI has become a habit; it’s hard to stop using it once you get used to it. It can save us a lot of time, and it is a real assistant for developers.

However, I see three main issues. First, the cost: with usage-based pricing, it can quickly become expensive. Second, the fear of losing certain skills over time with this kind of assistance, we might become too dependent on it. And finally, the risk of burnout. By accelerating certain tasks (sometimes even relaxing ones), we become more active overall and may risk overwork. Take care of yourselves!