I can help you master the delicate balance of trust calibration in AI to make smarter, more informed decisions—here’s how to avoid overtrust.
The “Trust Calibration” Skill: Using AI Without Overtrusting
Browsing Category
AI Literacy & Responsible Use
30 posts
AI in Customer Support: Why “Fast” Isn’t the Same as “Good”
Keen to understand why speed alone falls short in customer support and how AI can help deliver truly quality service?
Data-Safe AI Workflows: How to Avoid Leaks in Practice
When it comes to Data-Safe AI workflows, implementing robust security measures is essential to prevent leaks and protect sensitive information—learn how to stay ahead.
AI and Translation: Where Subtle Errors Come From
Theories behind AI translation errors reveal how subtle nuances often slip through, leaving you curious about how to bridge the gap effectively.
AI for Writing: How to Keep Your Voice Intact
Perhaps the key to preserving your unique voice with AI lies in this essential approach—discover how to stay authentic in every word you generate.
Ethics in AI Projects: 4 Questions That Prevent Harm
Unlock essential questions to prevent harm in AI projects and discover how to ensure ethical, fair, and responsible AI development.
The AI Double-Check List: 7 Times You Must Verify
But before relying completely, discover the seven crucial verification steps to ensure AI accuracy and ethical integrity.
Model Selection: When Smaller Models Are Better
Just choosing smaller models can enhance your results—discover why simplicity often beats complexity in data modeling.
AI Verification: The “Cross-Check Triangle” Method
Discover how the “Cross-Check Triangle” method ensures AI reliability by systematically testing robustness, data integrity, and validation consistency, and why it matters.
Prompt Injection: How Systems Get Tricked (Conceptually)
Great insights into prompt injection reveal how systems can be tricked—discover the clever methods behind these manipulations and how to stay protected.
