Prompt engineering is not just about asking models the right questions — it is about structuring those questions to think like a data auditor. When used correctly, it can make quality assurance faster, smarter, and far more adaptable than traditional scripts.
The overarching goal is to maximize the return on analytical talent, shifting their focus entirely from data preparation to predictive model development, which is a necessary move if the business intends to compete in an AI-driven economy.
You don’t need advanced skills to work with large datasets. With Python’s built-in features and libraries, you can handle large datasets without breaking a sweat even if you're a beginner.
