🧠✨ Data Quality: the Silent Antidote to AI Hallucinations

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In the world of artificial intelligence, we often talk about models, architectures and GPUs… but one element determines a system’s reliability more than any other: data quality.

• 📌 Without consistent, complete and verified data, even the best model can fail.
• 📌 Hallucinations aren’t “magical errors”: they are often symptoms of noisy, biased, or poorly structured data.
• 📌 Data traceability and lifecycle control are becoming pillars for any organization that wants reliable AI.

The article highlights how companies are adopting more mature data governance practices, automated validation and continuous auditing to reduce risk and improve model accuracy.

In a context where AI is integrated into critical decisions, data quality ceases to be a technical luxury and becomes an ethical and operational requirement.

🪄 Quick explanation

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Imagine AI as a chef.
If you give it fresh, clean and well-selected ingredients, it will cook an excellent dish.
But if it receives old, mixed or contaminated ingredients, the result will be poor, no matter how talented the chef is.

Data quality is exactly that:
👉 giving AI the best ingredients so it produces reliable results.