Artificial intelligence (AI) has transformed our lives in extraordinary ways. Think about healthcare, agriculture, education, entertainment, and, of course, software development. AI is present in all these sectors, streamlining tasks, optimizing processes, and opening doors to innovations that seemed impossible just a short time ago.

However, like any powerful tool, AI comes with risks. And I’m not here to discourage you from using it—quite the opposite! My aim is to raise awareness: using AI without understanding fundamental concepts can be a dangerous trap. 🛑

🧠 The Source of Data: The Heart (or Achilles' Heel) of AI

The great secret behind the success or failure of AI lies in the data used to train it. Data is the foundation of all its knowledge. But what if that data is inaccurate, biased, or even prejudiced? The result will reflect those flaws.

Imagine receiving medical advice based on incorrect or outdated information. That risk, though extreme, is a real possibility when blindly trusting AI-generated responses.

🤔 Interpretation: The Crux of the Matter

Have you ever thought about how crucial interpretation is in our daily interactions? How many times have you been misunderstood by family or friends? Now, imagine trying to explain a complex idea to AI—with all the nuances of context, tone, and intention.

The way you phrase your request can make all the difference in what the AI delivers. A poorly chosen word or an unclear context can result in irrelevant or, worse, completely wrong responses.

If interpretation is already a challenge among humans, imagine dealing with a machine that depends on specific parameters and patterns to understand your needs.

🚀 How Does This Impact a Developer’s Growth and Learning?

Learning to develop goes far beyond writing code, structuring decisions, using loops, or mastering object-oriented programming. It also involves practices like testing and, most importantly, security.

Does AI help with code generation? Absolutely! But beware: the code it generates often contains security flaws. When I say this, I’m referring to various vulnerabilities, especially those cataloged by organizations like OWASP and MITRE.

Can a better prompt reduce errors? Possibly, but it’s not a definitive solution.

Remember: where does AI learn to write code? From forums, portals, and online databases. However, as with any other topic on the internet, these sources are filled with false positives, outdated information, and practices that have become insecure as security standards evolve.

This is where your expertise comes in: knowing how to analyze, evaluate, and adjust AI-generated code to ensure your work is secure and reliable.

So, don’t just ask AI to generate something for you. Understand the technical reasoning behind what you’re requesting. Use AI as a tool to assist your daily work, but never as your sole source of knowledge. Continuously seek to learn from reliable sources like courses, books, and specialized communities.

Remember: all knowledge is valid and important, whether related to your field or not.

💡 A Practical Example

Take this text as an example. In its initial draft, it was 121 lines long. With AI assistance and a focus on making it more dynamic, I adjusted and significantly reduced the line count without losing the essential information I wanted to convey.

✅ Conclusion

Using AI is like driving a high-performance car: it can take you to incredible places, but it requires skill, care, and attention at the wheel. AI is not a substitute for knowledge but a tool to enhance it.

So, keep exploring, experimenting, and learning. But above all, maintain a critical eye and remember: nothing replaces a solid understanding of the fundamentals. 💪

If you'd like to read more texts, not just like this but on other topics, check out my profile or visit my GitHub. There, you’ll find content in pt_BR and on dev.to in English. 🌍

Final Observation: To complement the content discussed, I recommend checking out this post, which explores useful tags to enhance the quality of responses.