Kimi, Shadow AI, Machine Learning vs. LLMs, Prompt vs. Context Engineering, and Local Models

The AI Actually crew tackles the pressing concerns keeping enterprise leaders up at night: shadow AI infiltrating organizations, the crucial distinction between machine learning and LLMs, and why context engineering matters more than prompt engineering. Jim Johnson takes the moderator chair, joined by regular Mike Finley and special guests Andy Sweet (Advanced Models Practice Lead) and Shanti Greene (Head of Data Science and AI Innovation).

Topics covered:

• Shadow AI: Why employees bypass IT to use personal AI tools
• Machine Learning vs LLMs: Understanding when each technology wins
• Prompt vs Context Engineering: Moving beyond moving commas
• Local vs cloud models: When ownership makes sense (and when it doesn't)
• Agent operations and the challenge of model stability
• The surprising costs of AI tokens, especially for audio applications

Follow the Gang

Jim Johnson, AnswerRocket, Managing Partner - https://www.linkedin.com/in/jim-johnson-bb82451/
Mike Finley, AnswerRocket, CTO - https://www.linkedin.com/in/mikefinley/ 

Andy Sweet, AnswerRocket, VP Enterprise AI Solutions - https://www.linkedin.com/in/andrewdsweet/
Shanti Greene, AnswerRocket, Head of Data Science and AI Innovation - https://www.linkedin.com/in/shantigreene/ 

Chapters:

00:00 Introduction to AI Actually and the Team

01:59 Kimi Model Release: Should We Care?

06:59 Shadow AI Definition and Enterprise Impact

12:08 Leveraging Machine Learning and LLMs Together

26:00 Prompt Engineering vs. Context Engineering

28:13 Using LLMs to Write Prompts

29:56 Memory and Agent Ops

33:05 AI Literacy and Context Engineering

34:27 Stability and Model Changes

37:28 The Harvard MBA Analogy for AI Agents

43:01 Local Open Source AI Models: Pros & Cons

49:23 The Real Costs of Enterprise AI

Keywords: shadow AI, machine learning vs LLMs, context engineering, prompt engineering, enterprise AI, local models, agent operations, AI costs, Kimi model, AI governance