CEO & President Kyle and Graphic Designer & Brand strategist Kelsey explore how prompting has evolved from using AI like a “smarter Google” to structured strategies that deliver sharper, less generic results.

They break down the CRIT framework (Context, Role, Interview, Task), share why detailed context reduces hallucinations, and explain how prompt libraries and model memory speed up repeatable work. The conversation also dives into context engineering with tools like Microsoft 365 Copilot and Google Workspace Gemini to make AI outputs more relevant and secure.

Plus: common prompting mistakes, model comparisons, multimodal inputs, and how to onboard teams without losing brand consistency.

Listen now to level up how you work with AI.

00:00 Prompting Then vs Now: From “Smarter Google” to Strategic Skill

00:39 Why AI Sounds Vanilla: Averages, Models & AI Slop

01:33 Prompt Engineering & the CRIT Framework

02:35 Interview-Style Prompts: Fewer Hallucinations, Better Results

04:10 Garbage In, Garbage Out: Treat AI Like a New Hire

05:04 Let AI Help Write Prompts + Tools & Libraries

07:08 Why One-Liners Fall Flat (Contractor Analogy)

07:55 From Prompts to Systems: Templates & Model Memory

11:21 Context Engineering: Files, Memory & Workplace Data (Copilot/Gemini)

13:27 Over-Prompting: Context Limits & When to Reset

16:26 Set Outcomes, Don’t Micromanage

18:22 Smarter Models: Gemini & Claude Need Less Steering

19:06 Claude Opus vs ChatGPT: Speed vs Detail

20:27 Multi-Model Workflow: Use Each for Its Strength

21:20 Why New Models Feel Smarter

22:11 Ask AI to Improve Your Prompts

24:42 Planning Mode: Structured Builds & AI Interviews

26:13 Training Teams: Frameworks, SOPs & Safe Experimentation

31:47 Multimodal & Voice Prompting (Gemini’s Edge)

33:15 Wrap-Up & What’s Next