Deploying AI-generated code in production: best practices and responsibilities
Fatma Hfidhi
Analyst Developer
Who hasn’t ever asked an AI tool to generate an SQL query or a snippet of code to save a few minutes? In seconds, an answer appears, and often it seems perfectly correct. But is it really appropriate for our situation?
In many situations, the time saved is impressive. We’ve already asked an AI tool for an SQL query or a code snippet to quickly resolve a problem. In several cases, this allowed us to move forward much faster, but we also observed that some solutions required several adjustments before they could be used.
But this ease also raises a new question: what do we do with the code generated by AI once it is produced?
Because between getting code that works and having code that can be deployed in production, there is an essential step: human validation .
Highlighting human validation
AI doesn’t always understand our business context, our management rules, our data, or the specific constraints of our environment. Therefore, it may propose a technically sound solution that doesn’t precisely meet our needs.
That is why the use of AI should not simply consist of copying and pasting what it offers us.
This is often where the real work begins. The code seems to work, but do we really understand what it does? Does it meet the need? Have we tested the specific cases?
Distinguishing between the issues of confidentiality and responsibility
In our daily work, we must also keep confidentiality issues in mind. Before transmitting information to an AI tool, it is important to verify whether we have the right to do so and what information is truly necessary.
Even when using AI, the responsibility for the final result always rests with us. The tool may suggest a solution, but it’s up to us to ensure it’s appropriate and safe. We’re gradually moving from “writing every line of code ourselves” to “being able to effectively evaluate, adapt, and validate the code produced with the help of AI.”
Perhaps one of the most important skills to develop with the arrival of these tools is knowing how to use AI without losing your professional judgment.
The goal is therefore not to choose between human expertise and artificial intelligence, but to take advantage of the strengths of each.
Clearly announce the checklist
Before deploying AI-generated code to production, certain checks should be part of the validation process. These can be summarized in five simple actions:
- Understanding the generated code
- Control its quality
- Confirm the business result
- Ensure the code complies with security and governance requirements
- Keep the validation evidence
Behind these five actions lie very concrete questions: Do we truly understand how the code works? Does it comply with existing standards? Does it adequately address business needs? Does it meet security and governance requirements? Are the validations performed sufficiently documented?
These elements help to reduce risks and strengthen confidence in the solutions put into production
If any of these steps are missing, the code should generally not be considered production-ready.
AI as the starting point, humans as the guarantors
AI has become an excellent tool for accelerating certain tasks and quickly exploring potential solutions. However, like any tool, its value depends on how we use it.
In most cases, we will need to consider the AI-generated code as a starting point rather than a ready-to-deploy solution.
Our role remains to understand it, validate it and ensure that it truly meets the need before it goes into production.
AI tools may have supported the review of this content.
