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Using Frameworks for Prompts for Better Outcomes
Episode Summary
How can you get better, more reliable results from AI?
In this episode, Paolo and Aziza explore why effective prompting is about much more than asking good questions. We discuss how structured prompting frameworks such as ROSES, CRISP, CARE, TRACE, and APE can help data scientists provide clearer instructions, reduce ambiguity, and improve the quality of AI-generated outputs. We also share practical examples of applying these frameworks to real-world data challenges and discuss why designing how AI should think is often more important than the answer itself.
Episode Highlights
Key Takeaway
Don't just ask AI for answers. Design how it should think. Clear structure, explicit assumptions, and well-defined expectations lead to better outcomes and greater trust in AI-generated results.
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By Alexander Schacht and Paolo EusebiEpisode Summary
How can you get better, more reliable results from AI?
In this episode, Paolo and Aziza explore why effective prompting is about much more than asking good questions. We discuss how structured prompting frameworks such as ROSES, CRISP, CARE, TRACE, and APE can help data scientists provide clearer instructions, reduce ambiguity, and improve the quality of AI-generated outputs. We also share practical examples of applying these frameworks to real-world data challenges and discuss why designing how AI should think is often more important than the answer itself.
Episode Highlights
Key Takeaway
Don't just ask AI for answers. Design how it should think. Clear structure, explicit assumptions, and well-defined expectations lead to better outcomes and greater trust in AI-generated results.