AI

A teacher reached out with a very practical question: how to design assignments so it’s clear whether the work is truly the student’s—or a result mostly generated by AI. It’s a familiar story. In recent years, large-model assistants have become an everyday tool: they write code, suggest formulas, rephrase essays, and even “explain” solutions. AI itself isn’t the enemy—it saves time and expands what people can do. But in education, a gap appears: assessment often sees only the final answer, not the process—and it’s the process that builds real skills.

It’s Time to Run AI In-House

Artificial intelligence has stopped being “experimental” over the past couple of years—it has become a practical tool companies use every day. From automating customer support to assisting with management decisions, AI is now embedded into everyday workflows across many teams. Many organizations can no longer imagine daily operations without these technologies. But despite the value AI delivers, not everyone thinks through the risks—especially those related to confidentiality and data leakage.

Table of Contents

• Chapter 1: Introduction to Prompts
• Chapter 2: What a Prompt Is
• Chapter 3: A Prompt for ChatGPT
• Chapter 4: Refining the Request
• Chapter 5: Final Attempt
• Chapter 6: Summary and Takeaways

Introduction to Prompts

Table of Contents

• Chapter 1: CPU vs GPU for AI Training
• Chapter 2: CPU — a Versatile Workhorse
• Chapter 3: GPU — the Power of Parallel Processing
• Chapter 4: Advantages of GPUs
• Chapter 5: From Gaming to AI

CPU vs GPU for AI Training