Work With IA After 8 month/en

Six mois avec l’IA : entre passion, frustration et puissance décuplée

When I started using AI intensively, I had a rather strange feeling: I became fascinated by this imaginary friend capable of doing some things better than me, faster than me, but not always with as much perspective.

At first, it is exciting. Then, very quickly, it becomes frustrating.

Frustrating because AI moves fast, very fast. It suggests, it generates, it corrects, it documents. But it does not always understand the real context, the hidden constraints, the history of a system, or the small subtleties that can make a theoretically correct solution dangerous in production.

After about six months of working with AI every day, I understood something important: for now, we are complementary.

I really mean for now, because I am not sure this partnership will remain the same in the future. When we look at the work of people like Yann LeCun, who are trying to give machines a deeper form of reasoning, it almost feels like the story has not even really started yet. And yet, everything is already moving very fast.

AI IN MY WORK AS A CLOUD AND INFRASTRUCTURE DBA

In my work, I am a DBA and I mainly work in the cloud, with still a part of on-premise infrastructure. My role constantly requires me to move from one topic to another: databases, networking, operating systems, application design, security, automation, DevOps pipelines, scripting, monitoring, and troubleshooting.

A modern DBA can no longer limit themselves to writing SQL. They need to understand the complete ecosystem around the database.

With the massive rise of automation, this role is expanding even further. We need to work with new languages, sometimes return to older ones, understand cloud tools, industrialize deployments, make processes more reliable, and maintain consistency across increasingly complex environments.

Personally, I have become very comfortable with PowerShell to automate tasks in Azure. I also use Python when needed. Today, I create my own applications, modules, and tools to manage several thousand production databases.

In my case, we are talking about more than 4,000 databases.

With Azure Batch, Azure DevOps, well-designed pipelines, and a solid automation strategy, we can achieve almost surgical precision. We can orchestrate processes, manage exports, automate backups, standardize deployments, monitor errors, intelligently retry certain processes, and produce usable reports.

But all of this requires one essential thing: discipline.

VERSIONING HAS BECOME VITAL

One point I now consider fundamental is versioning.

When Microsoft, Oracle, Amazon, or others change something, even slightly, it can put the stability of a system at risk if everything is not properly controlled.

A module version, a package version, a script version, a pipeline version: everything must be clear, traceable, and reproducible.

Without versioning, we end up chasing problems. With versioning, we can understand, compare, roll back, and secure changes.

This is even more true when we start working with AI, because it can produce a lot of code very quickly. If we do not structure what it generates, we can quickly end up with chaos that is very well presented.

THE IMPORTANCE OF INSTRUCTION FILES AND AGENTS

What truly changed my experience with AI was not just asking questions to a chatbot. It was learning how to structure the context.

I use instruction files that define the working context for my AI sessions:

```mediawiki

• naming conventions;
• file structure;
• error handling rules;
• commenting standards;
• internal best practices;
• company logic;
• tags;
• pipelines;
• modules;
• project architecture.

```

I also started working with a logic of specialized agents.

For example:

• a PowerShell-oriented agent;
• a documentation agent;
• a SQL expert;
• an MSSQL expert;
• an Oracle expert;
• an agent for exports;
• an agent for project structure;
• an agent for troubleshooting.

On top of that, I use a kind of orchestrator agent that chooses the right expert depending on the topic.

This is where the experience becomes truly powerful. We are no longer simply asking AI: “write me a script.” We give it a framework, rules, history, a way of thinking, mistakes to avoid, and standards to follow.

And at that point, the quality changes completely.

ERROR FILES: A VERY VALUABLE TECHNICAL MEMORY

One of the most interesting aspects of my approach is error management.

When AI makes a mistake, I do not only want to correct the final result. I also want to prevent it from making the same mistake again in the next generation.

For this, I use Markdown files dedicated to known errors, traps already encountered, and corrections to apply.

These files become a kind of technical memory. They allow AI to know about past mistakes and better respect my constraints in the future.

This is particularly useful in automation scripts, where a small PowerShell scope issue, a forgotten parameter, a naming mistake, a wrong path, or an incorrect retry logic can have significant consequences.

With this method, AI does not become perfect, but it becomes much more useful. It improves with my context.

THE FEELING OF MANAGING A SMALL TEAM

Today, when everything is well structured, I sometimes feel like I am managing a team of several dozen people.

Not in the sense that AI truly replaces a human team, but in the sense that I can quickly call on several “profiles”:

• a PowerShell developer;
• a technical writer;
• a SQL analyst;
• a reviewer;
• a documentation specialist;
• a DevOps assistant;
• a troubleshooting expert.

It is quite magical.

But this magic only works if I remain in control.

AI greatly accelerates things, but it needs a pilot. It can generate quickly, but it does not always know what is acceptable in a production context. It can propose an elegant solution, but not necessarily one that fits the history of an infrastructure. It can give a convincing answer, but one that may be false or incomplete.

That is where human experience remains essential.

AN EXPERIENCE THAT HAS BECOME POSITIVE

After six months of intensive learning, testing, frustration, and adjustments, my experience with AI has become very positive.

I no longer see it as a simple tool for generating text or code. I see it as an accelerator for thinking, documentation, automation, and structuring.

It helps me move faster, explore more options, document better, standardize my practices, and produce more robust tools.

But it also forces me to be more disciplined:

• clearer in my instructions;
• more structured in my projects;
• more careful with versioning;
• more precise in my rules;
• more critical in my validations.

Ultimately, AI does not replace my profession. It greatly increases my ability to practice it.

AND TOMORROW?

I remain positive, but also cautious.

The evolution is so fast that it is difficult to predict where we will be in two or three years. If machines truly start to reason in a deeper way, the relationship between humans and AI could change very quickly.

Today, I feel complementary with AI.

Tomorrow, I do not know.

But one thing is certain: the story is only just beginning.

And for those who are willing to learn how to work with these tools, not just use them superficially, the potential is enormous.

For a DBA, an infrastructure engineer, a developer, or anyone who has to navigate across multiple technologies, AI is already a remarkable tool.

Not perfect. Not magical without methodology. Not without risks.

But when used properly, with context, rules, versioning, and real human supervision, it becomes an impressive lever.

After six months, I can say it clearly: despite the frustrations at the beginning, I would not go back.