The AI course built for business professionals,
not programmers.
200+ pages of practical guidance for managers, entrepreneurs,
and professionals who want to use AI at work. Without learning to code.
Written by a software developer with 20 years of experience
Most AI training teaches you about AI.
This one teaches you to use it for your actual work.
There are hundreds of AI courses out there. Most of them fall into one of three categories: university-style content that's too theoretical to apply, YouTube tutorials that contradict each other, or video courses where you watch someone click through a tool for six hours and remember nothing by Monday.
The common thread: they're not built for people who run things. They're built for people who build things. If you're a manager, a business owner, or a professional who needs to make decisions about AI, the market has largely left you behind.
86% of employers worldwide expect AI to transform their business by 2030. Yet only 14% find training that actually works. That's not a content problem. It's a design problem. Almost no one has built an AI course for the person who needs to use it, not explain it.
This course fills that gap.
of professionals say AI training is crucial
of employers expect AI to transform their business by 2030
find training that actually works
A reference manual you use at work, not a video you forget.
"From User to Orchestrator" is a 200+ page PDF written for business professionals. Eight modules covering everything from AI fundamentals (what it actually is, without the hype) to autonomous AI agents, including prompt engineering, tools (ChatGPT, Claude, Gemini), personal productivity, business ROI, team management, and Claude Cowork.
It's not a video course you watch passively and forget. It's a manual: you search it, open to the chapter you need today, and apply it. You download it once, you keep it forever. Educational subscriptions lose up to 64% of their members within a year. This PDF is yours permanently.
Written by a practitioner
20 years as a software developer, then AI consultant. Everything in this course has been used in real work, not sourced from blog posts.
Complete, not curated
From AI basics to agentic systems, in one place, without ever getting technical. Every concept explained with a concrete analogy.
PDF format
Searchable, annotatable, printable. Open to the right chapter in 10 seconds. Not scrubbing a video timeline.
Part of a live ecosystem
The AI Architect is not just a course. It's an active weekly newsletter that tracks what's changing in this space as it changes.
Eight modules. 200+ pages. Everything you need, nothing you don't.
Every module stands alone. Start with the chapter that's relevant to your work today, apply it, come back when you need more.
AI doesn't think. This module cuts through the noise: what AI actually does under the hood (no technical jargon), what it does well, what it does badly, and what it can't do at all.
- AI doesn't think: what it actually does under the hood
- What AI does well, what it does badly, what it can't do
- The current landscape: ChatGPT, Claude, Gemini and the rest
- The "AI takes jobs" myth: the honest version
The gap between people who get mediocre results from AI and people who get excellent results is almost always here.
- The structure of a perfect prompt: context, role, objective, format
- Iterative prompting: refining until you get the result
- Few-shot prompting: teaching by example
- Meta-prompting: getting AI to write the prompt for you
- Working in sequence: complex multi-step tasks
- The most expensive mistakes (and how to stop making them)
- Sycophancy: when AI always agrees with you
- Structuring with XML tags
They're not all the same. This module helps you choose the right tool for the right task, understand when it's worth paying, and navigate the landscape without testing everything yourself.
- ChatGPT: strengths, limitations, when to use it
- Claude: strengths, limitations, when to use it
- Gemini: Google's AI and the ecosystem advantage
- Grok, Copilot, Perplexity and the rest
- Free vs. paid: when it's worth paying
- How to choose the right tool for the right task
The practical applications that change how you work starting tomorrow.
- Email: writing, responding, managing your inbox
- Documents and reports: from draft to final text
- Research and synthesis: read less, understand more
- Meetings and notes: transcriptions, summaries, action items
- Data analysis: making your spreadsheets talk
- Multimodality: AI with voice and vision
- Building custom tools without coding
- AI as a personal tutor
Stop talking about AI and start measuring it.
- How to calculate the value of AI in your organization
- Where to start for maximum impact
- Where AI disappoints and why
- How to present AI to your board or clients
- Privacy, hallucinations, copyright
- The AI capability diagnostic (Capability Dissipation Gap)
How to introduce AI to a team that doesn't want to change, build scalable workflows, and redefine the manager's role.
- AI as a team member: redefining who does what
- How to introduce AI to a resistant team
- Delegating to AI: what works, what doesn't
- Repeatable, scalable AI-first processes
- How KPIs change in the AI era
From chat to action: what changes when AI doesn't respond but does.
- Six levels of autonomy: from autocomplete to dark factory
- Agents in the work you know: real cases
- What goes wrong: risks and what you never delegate
- Intent engineering: the level almost no one builds
- Specification engineering: the specification as infrastructure
A dedicated module on the tool that brings agentic AI directly to your computer.
- From chatbot to agent on your desktop
- How it actually works: files, folders and control
- Practical workflows: what to delegate and how
- The connected ecosystem: skills, MCP and Chrome
- Guardrails, limits and where we really are
- Appendix A - Essential glossary (35 terms explained in plain language)
- Appendix B - Prompt library by use case (9 ready-to-use categories)
- Appendix C - Recommended resources (tools, newsletters, accounts to follow)
- Appendix D - Reading AI numbers without getting misled (5 data literacy rules)
Three chapters you won't find in any other AI course.
Most AI courses stop at prompt engineering. This one doesn't.
The Capability Dissipation Gap
A framework for diagnosing where your organization is losing the value that AI could be creating. Four structural inertias that block adoption, a 16-question self-assessment scorecard, a 2x2 matrix to identify which quadrant your company sits in, and action plans for each. Not theory: a diagnostic tool ready to use.
Intent Engineering
The layer of prompting that almost no one builds. It's not the prompt you write: it's the intention you're trying to transmit. Three real cases: Morgan Stanley (how it's done right), Klarna (how an intent gap cost millions), Wells Fargo (the counterexample). And the concept of SOUL.md.
Specification Engineering
The specification as infrastructure. Real data: Goldman Sachs with 12,000 users and a documented 30% time saving, Adobe using specifications to manage OKRs across 30,000 employees, Gartner data showing 47% of agentic AI projects get cancelled due to inadequate context delivery. And five concrete primitives for building specifications that actually work.
Who this course is for. And who it's not.
✓ This course is written for you if:
- You're a manager, director, or team lead who needs to understand AI well enough to guide decisions and people
- You're a business owner or entrepreneur who wants to know where AI creates real value in your business (and where it's just noise)
- You're a professional (consultant, lawyer, accountant, marketer, HR) who already uses ChatGPT but suspects you're not getting the most out of it
- You've heard a lot about AI but feel like you're missing a framework to understand it properly
- Your industry is being reshaped by AI and you want to understand the implications before everyone else does
✗ This course is not for you if:
- You're a developer or engineer: you won't find Python code, system architecture, or neural networks
- You're looking for a video course: this is a PDF
- You want to become an AI researcher or data scientist
Who wrote this. And why it matters.
I'm Matija Vidmar. I spent 20 years as a software developer before becoming an AI consultant. Those years were spent building real systems in real environments, not writing about technology from the outside.
When generative AI changed the industry, I started working with professionals and organizations to understand where AI genuinely creates value, and where it's just well-marketed noise. What you'll find in this course comes from that applied work, not from academic papers or YouTube tutorials.
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Course
One-time payment, yours forever
- Complete PDF course, 200+ pages
- 8 modules + epilogue + 4 appendices
- Prompt library (Appendix B) with 9 ready-to-use categories
- Permanent access to your private area
- The PDF is yours forever. No subscription, no renewal
Course + Updates + Newsletter
Annual subscription
Less than €11/month to stay current in a field that changes every quarter.
- Everything in Course - complete PDF, 200+ pages, prompt library, and permanent private area access.
- AI changes every 3-6 months. Your course updates automatically with it.
- Exclusive weekly newsletter: what changed in AI this week, what it means for your work, what to do about it. Curated by the author.
- Early access to new chapters and modules added to the course.
- Cancel anytime. No lock-in, no penalty.
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For professionals who want to understand AI before everyone else does.
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