Artificial intelligence has become much more than just a technological gadget; it has redefined my IT practice. With over 10 years of experience, my role has changed radically.

In my early days as a junior developer, my day-to-day life was all about manual execution: I invested all my energy in mastering syntax, debugging line by line, and absorbing new frameworks. As I progressed to a senior role, my focus shifted to architecture, system robustness and mentoring within the team. I have to bridge the gap between technical requirements and business needs, while managing customer expectations and tight deadlines. This is where AI becomes an essential lever. It doesn't replace my expertise; it increases my "bandwidth".

By delegating low-value-added tasks to AI, such as boilerplate code generation, writing exhaustive unit tests or the first draft of API documentation, I free up precious time. This gain in efficiency allows me to focus on what really matters: critical validation, strategic alignment and the delivery of solutions that truly meet customer challenges. For me, AI is not a threat; it's the tool that enables me to deliver value faster, without ever compromising the quality of the final product.

My workflow: from execution to strategy

Before AI became part of my toolbox, my role as an individual contributor required me to be present in every nook and cranny of the code. Every step depended on my direct intervention; I was often the bottleneck in my own projects. Today, my role has evolved: as a senior engineer, I can no longer (and must no longer) code everything myself. I have to trust my team, and AI has become an extension of that team, acting as a tireless teammate.

However, this fluidity isn't magic. We often hear that AI speeds everything up, but it's a double-edged sword. If you send a fuzzy command to the AI, the risk of receiving an invalid implementation is much higher, and it will take longer to correct than to code from scratch. The speed gain comes not from the tool itself, but from my ability to popularize complex requirements.

This is where my leadership skills come into play: the precision of my prompting and my good work habits determine the quality of the output. It's no longer a question of "coding faster", but of steering the machine better to avoid unnecessary back-and-forth. The creative steps remain the same, but my approach has become more strategic: I'm no longer the one typing each line, but the one orchestrating the solution, ensuring the technical intent is perfectly translated.

1. From solitary refinement to enhanced clarification

Traditionally, the refinement of a user story relied on exhaustive reading and risk analysis, entirely on my shoulders or those of the team. We had to go through the requirements with a fine-tooth comb to flesh out grey areas and anticipate collateral impacts. It's a crucial stage, but one in which human error, an overlooked exception or a technical constraint are always lurking.

Today, AI acts as a sparring partner during this phase. It doesn't do the analysis for me, but it challenges me. It allows me to slow down to better understand the need before jumping headfirst into the code.

For example, when refining a complex task, I don't hesitate to ask her targeted questions:

• "Here are the acceptance criteria for my story: [insert text]. What are the technical edge cases or borderline scenarios we might forget?"
• "Act like a rigorous QA tester: what unit and integration tests should I plan to validate this calculation logic?"
• "Analyze this feature request: are there ambiguities in the wording that could lead to misinterpretation by the dev team?"

This approach transforms refinement into a dynamic dialogue. AI helps me ask the right questions earlier in the development cycle, reducing subsequent rework. It's become my safety net, ensuring that when the team starts coding, the direction is clear and any grey areas are cleared up.

2. From manual navigation to intelligent planning

Previously, finding the right code to change relied heavily on in-depth knowledge of the architecture and manual navigation across several files. It was a demanding exercise that required memory and patience. Today, AI greatly accelerates this process, but it still needs a clear framework; otherwise, it can lead the analysis astray.

If used without tags, AI tends to operate without taking a step back: it generates code quickly, but without regard for the overall architecture or the patterns already in place. To get real value out of it, I use it more as a thinking engine. By activating its reasoning capabilities, for example, with planning prompts, it doesn't just generate lines of code: it proposes a detailed game plan.

It helps me validate the best way to implement a feature while respecting established design principles, such as SOLID and Clean Code. This exchange allows me to put my ideas to the test: "If I make this change to this service, what impact will it have on existing dependencies?" By forcing me to structure my thinking, AI acts as a checkpoint, helping me confirm that the proposed solution fits well with our technical ecosystem rather than being an isolated patch.

3. From manual implementation to assisted execution

Writing code and creating tests, whether you're into TDD or not, have always been phases that require concentration so as not to let anything slip through the cracks. I used to write the entire test suite manually.

Today, my approach has become hybrid and much more efficient. I usually start by writing the first critical tests or the most complex test cases myself. This is my way of "setting the tone", defining the technical tags and ensuring that the design intent is clear. Once this template is well established, I let the AI handle the remaining tests to be written, with a review from me at the end.

Artificial intelligence is formidable at generating repetitive code drafts or running a series of unit tests on data variations. However, the decision to delegate remains in my hands: I choose what to entrust to the machine based on the module's complexity. This allows me to preserve my mental energy for the creative aspects and engineering challenges where my expertise is irreplaceable. At the end of the day, I'm no longer the one "typing code" on the assembly line, but the one who validates that the solution is robust.

4. The code review: arriving in front of the team with a finished product

Peer review is, and will remain, a non-negotiable step in guaranteeing quality. However, in a senior role, you often have to "blaze the trail" on your own. Whether it's due to a lack of immediate availability from colleagues or because you're in the ideation phase of a new module, it's not always possible to carry out every reflection or architecture plan as a team.

This is where AI changes the game. Even before I call on my colleagues' precious time via a Pull Request (PR), AI acts as a rigorous first filter. It performs an internal validation to identify potential problems, suggest performance optimizations or spot security flaws before they leave my desk.

This preliminary stage cleans up what we call "nitpicks": those little details of form or inattention that often pollute code reviews. By eliminating this background noise, I avoid unnecessary back-and-forth with the team. As a result, when my peers review my code, we can concentrate on the essentials: strategy, business logic and overall vision. Rather than wasting time on peccadilloes. It's this mutual respect for each other's time that makes the feedback cycle so much richer and more effective.

AI in action: my use cases

Beyond the big concepts and theory, how does AI fit into my day-to-day life? It's not a magic wand that fixes everything with a single click, but rather a strategic lever that I activate according to the challenge in front of me. Whether I'm deep in code, planning the next phase of a project or managing customer expectations, AI has become the most versatile tool in my toolbox.

It allows me to spend less time on mechanics and more time thinking. Here's an overview of how I work with AI to optimize every facet of my work, from the most repetitive tasks to the most critical architectural decisions.

1. Interface refinement and stylization (UI)

AI has become an invaluable ally in fine-tuning the visual and functional aspects of my components. Rather than iterating endlessly on CSS details or state logic, I delegate some of the work to her within a clear framework.

Example: for a Dropdown.tsx component, I provide the Figma link, the project standards (design tokens, accessibility, React conventions), and an extract of the existing code. The AI can then analyze the current implementation and propose a state logic consistent with our mock-ups (or point out discrepancies), while suggesting targeted CSS adjustments.

You're a senior front-end developer (React + TypeScript). I'm going to give you:

1) a Figma link (text description included),

2) the project standards,

3) the current Dropdown.tsx code.

Objective: propose a concrete game plan (steps) to align the component with the model.

Constraints:

- Don't break the component's public API.

- Respect accessibility (keyboard, focus, aria).

- Avoid adding dependencies.

- Keeps style consistent with our tokens.

Responds with :

- Diagnostics (deviations from mock-up + risks)

- 5-8step plan

- List of changes to be made (UI, status, events, CSS)

2. Code review

For my code reviews, I don't just ask the AI: "What do you think?" I use what's known as a persona: a set of specific instructions (a System Prompt) that defines the AI's role, tone and priorities.

In concrete terms, I "program" my assistant to act like a Staff Engineer obsessed with robustness and maintainability. I give him clear guidelines on the structure of his feedback: he must classify his comments by level of severity:

• [CRITICAL]: Logic bugs, security flaws or major architectural problems.
• [IMPORTANT]: Performance optimization, code readability or compliance with design patterns.
• [NICE TO HAVE]: Style suggestions or micro-optimizations (the famous nitpicks).

This structure allows me to sort the wheat from the chaff at a glance. If the AI comes up with ten "nice to haves", I know my code is solid. But if it finds one "critical", I stop right there.

The "Cross-Review" trick: To avoid blind spots, I apply a counter-expertise strategy. AI models each have their strengths and biases. If I've used Claude (renowned for his penmanship and code logic) to generate a feature, I often ask GPT-4o to do the review. Changing digital "brains" allows me to gain a different perspective and detect errors that a single model might have overlooked out of complacency. It's like asking a colleague who hasn't worked on the project for a second opinion: that's when the best criticisms come out.

3. Understanding unfamiliar code (onboarding)

Arriving on a new project is a challenge. You're embarking on a sometimes totally new business domain, and you're doing so in a project that has a complete history of technical choices made on the code. How do I know if I've accounted for all the important parts of the code to implement the features? To help me, I ask the AI to "scan" the source code to extract architectural patterns and identify critical files. This allows me to become productive much more quickly.

Example prompt:

I'm a senior developer integrating this project.

I would like you to go over the #codebase for this project to help me understand the key features, the coding patterns used, what to look for, and which parts of the code you consider critical.

Please write down all your findings in Markdown files. I'll repeat myself :

- Pattern used in code

-3rd party integration

- How is the code structured (API / Services/ Database)

- Anything useful so I can get up and running fast in the project

This prompt allows you to generate one or more separate files and quickly identify patterns in the code.

4. Debugging: break the deadlock faster

Faced with a tough bug, it's easy to go round in circles for hours trying to reproduce erratic behaviour. At times like these, AI becomes a valuable ally in getting us out of a bind.

If I already have logs to hand, the AI is often able to pinpoint the problem in a matter of seconds, whereas I would have spent an hour combing through execution traces. And when the source is completely unknown, I use it to develop reproduction scenarios or to suggest strategic places to inject additional logs. It doesn't fix the code for me, but it speeds up the investigation considerably.

However, this speed of diagnosis implies a responsibility: not to accept the first solution that comes along. This is where the boundary between tool and expert becomes clear.

Humans in the loop: mastering AI, not submitting to it

With over ten years of experience behind me, my greatest asset is no longer my typing speed, but my ability to understand a system in its entirety. It's this expertise that enables me to judge AI production with discernment.

You have to be on your guard: AI may very well come up with a solution that "compiles" and fixes the bug on the surface, but introduces a monumental technical debt or security hole elsewhere. Mastering AI means knowing when to say "no".

I'm happy to delegate if the patterns are clear or if the task is repetitive (the famous boilerplate). On the other hand, I keep a hand on critical architectural decisions and on the heart of business logic. Why do I do this? Because AI is not aware of the product's long-term stakes or of the human realities of its users. It speeds things up, but it's my critical eye that transforms its suggestions into a robust solution. She's the engine; I'm the pilot.

Conclusion: humans at the helm of innovation

In short, artificial intelligence has not replaced my role as a developer; rather, it has enhanced it. By acting as a strategic ally, it frees us from the most mechanical tasks and considerably accelerates our feedback loops. This gain in speed enables us to validate hypotheses in record time and focus on what really matters: solving complex problems and creating raw value for our customers.

This evolution marks a new era for our profession. The role of development teams is shifting from syntax expertise to solution architecture. Today, mastering these tools is no longer optional; it's a fundamental skill for staying relevant in an industry that's moving at lightning speed.

I encourage every developer, regardless of seniority, to experiment with and understand these technologies. Learn how to push, test and integrate them into your workflow. But always keep one thing in mind: AI is a phenomenally powerful engine, but it has no compass. It's your expertise, ethics, and vision that set the course.

AI may be your new co-pilot, but never forget that you remain the pilot.