Everyday AI Podcast – An AI and ChatGPT Podcast

How did prompt engineering die so quickly? ☠️

And what the heck does context engineering even mean? 

One of the trickiest things about LLMs is they're changing daily, yet they're the engines that drive business results. 

But if the engine is constantly changing, then you also have to change how you drive and the roads you take. 

That's why we're tackling context engineering in this installment of our Start Here Series, the essential beginners guide to understanding AI basics and growing your skills. 

Context Engineering: How to Get Expert-Level Outputs From AI Chatbots -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
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Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
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Topics Covered in This Episode:
Evolution from Prompt to Context Engineering
Why Prompt Engineering Is Now Obsolete
Defining Context Engineering in AI Chatbots
Six-Part Framework for Context Engineering
Four Layer System for Structuring AI Context
Building Reusable Context Vaults and Skills
Connecting Business Data to AI Models
Techniques to Achieve Expert-Level AI Outputs
Importance of Context Windows in Large Language Models
Context Engineering Best Practices and Scalability

Timestamps:

00:00 "Access AI Community & Tools"
03:08 "Mastering Context in AI"
07:23 "Smart Models Require Less Precision"
12:01 "Context Engineering Beats Prompt Engineering"
15:49 "AI Context: Six Key Blocks"
16:47 "Building Context for Better Results"
19:53 "AI: Training, Not Easy Button"
25:17 "Chain of Thought Prompting Decline"
29:11 "Show, Don't Tell Techniques"
32:13 "Context, Reuse, and Scalable Systems"
33:19 "AI Chatbots: Memory and Skills"

Keywords: 
context engineering, AI chatbots, expert level outputs, prompt engineering, large language models, business context, AI models, custom instructions, data access, context window, prime prompt polish, reusable context vaults, context vaults, skills file, memory enabled models, ChatGPT, Claude, Google Gemini, Microsoft Copilot, connectors, apps, searchable index, business data, personalized AI, context clues, reference material, examples, procedures, evaluation rubric, chain of thought prompting, generative AI, nondeterministic behavior, show don’t tell technique, few shot examples, rubric first technique, grading criteria, output quality, scalable AI systems,
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Like 99% of companies are pushing AI. 🚀

But like 0.01% are actually training their people on it. 🤦

Don't worry. We'll go over the essentials on how to get started training your team or getting your company trained on best practices for using AI in your day-to-day. 

First step to solving the AI implementation gap starts here. 

How to Train Your Team on AI: The 7 Steps to Educate Your Organization on LLMs -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
AI Training Adoption Gap in Organizations
Seven Steps to Train Teams on AI
Importance of Leadership in AI Adoption
Fixing Broken Workflows Before AI Integration
Choosing and Committing to One AI Platform
Training Employees in Three AI Knowledge Layers
Documenting Procedures for AI Implementation
Mandating Hands-On AI Practice and Outputs
Transitioning Teams from AI Operators to Orchestrators

Timestamps:

00:00 "AI Spending, Training Gap Looms"
05:48 "Embracing AI-First Leadership"
08:43 "Choosing Between AI Copilots"
10:22 "Unified AI Operating System Strategy"
14:46 Partnering for Generative AI Success
18:40 "Document Processes for AI Workplaces"
19:58 "AI OS for Decision Trees"
24:56 "Future Jobs: Orchestrating Tasks"
27:33 "Training Teams on AI"

Keywords: 
AI training, large language models, corporate AI adoption, AI literacy, AI education, team AI training, employee AI training, organization AI strategy, AI workflows, workflow optimization, AI platform selection, AI operating systems, Microsoft 365 Copilot, ChatGPT Enterprise, Google Gemini, Anthropic Claude, shadow IT, AI tool sprawl, data documentation, procedure documentation, unstructured data, domain-specific AI training, AI upskilling, AI reskilling, hands-on AI practice, AI hackathon, change management, cultural adoption, AI leadership, CEO AI usage, champion teams, department AI training, AI ROI, measurable AI outputs, AI orchestration, operator to orchestrator, expert-driven workflows, AI agent, AI mindset shift, AI native workplace, AI best practices, AI adoption gaps, enterprise AI maturity
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Let's talk about the AI elephant in the room: hallucinations. 🐘

Maybe hallucinations are the reason your company has been hesitant on AI. 

But here's the thing, y'all. If you know what you're doing, hallucinations are largely manageable. 

But first, you gotta understand what they are, how they happen, and how to reduce the risk. 

Let's get started cutting down hallucinations together. 

AI Hallucinations: What they are, why they happen, and the right way to reduce the risk (Start Here Series Vol 5) -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
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Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
AI Hallucinations Definition and Causes
Large Language Models' Hallucination Mechanisms
Hallucination Types: Fabricated Claims & Sources
Model Improvements Reducing Hallucination Rate
Context Window Impact on AI Accuracy
AI Hallucinations in Legal and Enterprise Settings
Four-Layer Method for Minimizing Hallucinations
Custom Instructions and Retrieval-Augmented Generation
Expert-Driven Verification and Agent Safety Practices

Timestamps:

00:00 "Modulate's Velma: Smarter AI Insights"
03:18 "Reducing AI Hallucinations Explained"
08:36 "Minimizing AI Hallucinations with Skill"
12:30 "Model Retention and Recall Decline"
13:23 AI Advances: Improved Accuracy and Recall
19:24 "AI Hallucinations and Their Causes"
21:07 "Customizing AI Behavior Effectively"
24:47 "Connecting Data to Reduce Hallucinations"
28:47 "AI Oversight and Expert Input"
30:56 "Reducing AI Hallucinations Simplified"

Keywords: 
AI hallucinations, large language models, next token prediction, AI error, human error, fabricated claims, reinforcement learning with human feedback, context window, context engineering,
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Spending more time fixing your AI outputs then you're saving? 

You're not alone. 

The trap? 

You're in operator mode. Falling for the industry status quo like upskilling and human-in-the-loop. 

The real winners in the AI race? 

Companies that have changed the human-AI relationship. 

How? 

Join us for Volume 4 of our Start Here Series as we uncover what you need to know. 

Human-AI Collaboration: Best practices for working alongside AI -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
Human-AI Collaboration Best Practices 2026
Shift from Operator to Orchestrator Roles
Human-in-the-Loop Limitations Explained
Expert-Driven AI Review Loops vs. Generic Oversight
Orchestrating AI Agents for Business Productivity
Building Reusable AI Context and Skills
Elevating AI Champions on Team
Human Strengths vs. AI Strengths in Workflows
Avoiding Augmentation Debt and Workflow Pitfalls
Mindset Shifts for Effective AI Management

Timestamps:

00:00 "Everyday AI: Start Here"
03:23 "AI Shift: Operator to Orchestrator"
06:35 "Unlearn to Harness AI"
11:15 "AI Surpassing Human Collaboration"
15:11 Expert-Driven AI Process Loops
18:10 "Expert Collaboration Boosts AI ROI"
23:59 "Outsmarting AI Through Expertise"
26:30 "Navigating AI Success Strategies"
31:19 "Embrace AI, Elevate Your Team"
32:18 "Embrace AI, Elevate Humanity"

Keywords: 
Human-AI collaboration, AI best practices, working alongside AI, human-AI relationship, AI orchestration, AI orchestrator, shift from operator to orchestrator, agentic workflows, AI agents, digital agents, expert-driven loops, expert oversight, senior partners with AI, context engineering, AI processes, context vaults, AI skills files, company data, chain of thought review, large language models, AI-powered workflows, AI expertise, AI in business, AI productivity, AI risk management, human in the loop, upskilling, reskilling, unlearning, AI mindset shift, augmented intelligence, multi-agent systems, AI automation, organization AI strategy, context quality, AI champion, domain experts, AI team integration, competitive advantage with AI, process redesign for AI, AI-powered decision making, accountability in AI, empathy in AI, ambiguous decision-making, novel judgment.
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One of the biggest mistakes in AI? 

Thinking that your company's AI use is noteworthy. Or, even a competitive advantage. 

It's not. 

We break it down in Volume 3 of our 'Start Here Series.' 

AI as an Operating System: LLMs Are the Internet Now -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Join the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
AI As An Operating System Explained
Large Language Models Replace Traditional Apps
AI Integration in Knowledge Work Platforms
Choosing the Right AI Operating System
Microsoft Copilot vs. Google Gemini vs. Claude vs. ChatGPT
Agentic Browsers Powering Autonomous Workflows
Model Context Protocol (MCP) for AI Agents
Orchestration Layer and Agent Collaboration
ChatGPT Apps Merging AI and Internet
Enterprise Data Integration with AI Tools
Context Switching Reduction Through AI Agents
Strategic AI Adoption and Platform Redundancy

Timestamps:

00:00 "AI: A New Operating System"
03:58 "AI Transforming Work Interfaces"
06:41 "Collaborating in AI-Native Workspaces"
12:25 Anthropic's Innovations in AI Tools
13:46 "OpenAI's Strategy and Market Focus"
18:02 "Cognitive Evolution Through AI"
20:57 "Agentic Browsers: Key 2025 Advancement"
25:12 Improving Content Through Data Insights
26:42 "Anthropic's MCP: The AI Connector"
32:19 "AI Tools for Productivity Integration"
34:20 "AI: Unlocking Context and Efficiency"
36:32 AI Governance and System Portability
39:35 "AI Operating System Insights"

Keywords: 
AI operating system, large language models, LLMs, AI as infrastructure, enterprise AI, AI adoption, agentic workflows, AI agents, orchestration layer, Copilot, Microsoft 365 Copilot, Google Gemini, Gemini business, Gemini enterprise, Anthropic Claude, Claude cowork, MCP, model context protocol, OpenAI, ChatGPT, ChatGPT apps, ChatGPT business, ChatGPT enterprise, AI native, dynamic data integration, productivity with AI, collaboration tools, agentic browsers, autonomous AI agents, context window, memory and personalization, expert-driven loops, app hop tax, context switching, AI integration in business, AI tools for teams, AI platform selection, data governance, modular AI workflows, permissions and audit logs, backup and redundancy in AI, competitive advantage with AI, 
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