Bad AI Prompts Get Bad Answers (How to Prompt Securely)

Some people struggle to get real value out of AI. Most of the time, it’s not because the model is bad, it’s because the prompt is.

In this video, I break down how to write better AI prompts using a simple, security-first framework. You’ll learn how to get clearer, more accurate results from AI tools without oversharing sensitive information or pasting things that don’t belong outside your environment.

This isn’t about “prompt hacks” or tricks. It’s about thinking clearly, structuring your request, and giving the model what it actually needs and nothing more.

What you’ll learn in this video:
- Why vague prompts lead to weak or incorrect answers
- Why AI “guessing” can lead to bad decisions
- What NOT to paste into AI prompts (passwords, API keys, internal data)
- How to safely abstract sensitive details using placeholders
- A simple prompt framework: role, goal, constraints, output, and safe context
- A real example comparing a bad prompt vs a good prompt
- Why more relevant context improves results without increasing risk
- How image prompting mirrors text prompting

If you use AI at work and especially in IT, cybersecurity, or technical roles, this approach will help you get better answers while staying disciplined about what you share.

Clear, relevant context leads to better answers.
Disciplined prompting keeps your data safe.

Timestamps:
00:00 Intro
00:17 Why Context is Important
00:38 What NOT to paste into AI prompts
00:55 How to abstract sensitive information safely
01:05 The prompt framework: role, goal, constraints, output, safe context
01:39 Bad prompt vs good prompt explained
02:07 Example of a bad prompt (and why it fails)
02:42 Example of a good prompt
03:50 Comparing outputs: weak vs strong results
04:48 How context improves you AI output
05:00 Image generation analogy for prompting
05:17 Closing