Guardrails and Reducing Hallucinations

The AI gap will kill companies.

What is it? 

it's the large divide between AI's crazy impressive capabilities and what most companies are actually using them for. 

And one of the biggest reasons for the AI gap? 

Talking. 

Like... no one understands how to talk about AI because the technology changes faster than Usain Bolt in Beijing. 

You wanna talk to your AI team about LLMs? 

PFT. They're running Ralph Wiggum loops in Claude Code and just kinda reading the code before it hits production. 

Yeah, the divide is WIIIIIDE. 

So we're gonna tackle it together on the second volume of our Starter Series: AI Without the Jargon: The AI Language Every Business Leader Needs to live by in 2026 -- An Everyday AI Chat with Jordan Wilson

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Topics Covered in This Episode:

1. AI Jargon Barrier for Business Leaders
2. Generative AI Basics and Lingo Breakdown
3. ChatGPT, Claude, and Gemini Model Comparison
4. Understanding Tokens and Context Windows
5. Large Language Models: Prompt to Outcome
6. Parameters, Model Power, and Cost Implications
7. Retrieval Augmented Generation (RAG) Explained
8. Embeddings, Vector Databases, and Chunking
9. Agentic Models vs. Transformer Models
10. AI Risks: Hallucinations, Prompt Injection, Guardrails
11. Model Context Protocol (MCP) and Connectors
12. Scaffolding for Complex AI Workflows
13. AI Success: ROI, Risk, and Implementation Strategies

Timestamps:

00:00 "Join Start Here Series Community"

03:21 Bridging AI and Business Leaders

09:35 Partnering for Generative AI Success

12:26 "AI Models Operate Using Tokens"

15:41 "Shift to Smaller AI Models"

18:56 "Understanding RAG and Its Impact"

22:38 "AI Tools Connecting via MCP"

24:56 "Minimizing AI Hallucinations Effectively"

28:45 "Fast, Careful AI Implementation"

30:53 "AI Guide for Business Leaders"

Keywords: 

AI language, AI jargon, artificial intelligence terminology, AI lingo, large language model, generative AI, prompt engineering, context engineering, context window, tokens, tokenization, model architecture, model parameters, neural network connections, 

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