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Free Interactive Course

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Stop guessing which AI tools to buy, which models to pick, or how agents actually work. This free interactive course gives you the exact playbook — built for founders and operators, not engineers. No theory. No jargon. Just the decisions that move revenue, with live demos you can play with right now.

By Julius Alba·12 modules·~80 minutes·Free

Every vendor is slapping "AI-powered" on their product. Most of the time, it's a glorified if-else statement with a chatbot skin. Before you spend a dollar on AI, you need to understand what's real and what's marketing.

What Large Language Models (LLMs) Actually Do

LLMs like GPT-4, Claude, and Gemini are pattern-matching engines trained on massive amounts of text. They predict the next most likely word in a sequence — billions of times over — which creates the illusion of understanding. They don't "think." They don't "know." They generate statistically probable text based on their training data.

Think of an LLM like the world's most well-read intern. It's seen everything, can summarize anything, and writes well — but it doesn't have judgment, experience, or accountability. That's where human oversight (and proper system design) comes in.

The AI Spectrum

Not everything called "AI" is the same. There's a spectrum from simple automation to full autonomous agents:

  • Rule-based automation (Zapier, Make) — "When X happens, do Y." No intelligence, just triggers.
  • Machine learning — Pattern recognition on structured data. Spam filters, recommendation engines.
  • Large Language Models — Natural language understanding and generation. ChatGPT, Claude, etc.
  • AI Agents — LLMs with tools, memory, and the ability to take actions autonomously. The frontier.

Traditional Software

  • Follows exact instructions
  • Breaks on unexpected input
  • Scales by adding servers
  • Costs are predictable

AI / LLMs

  • Handles ambiguity and context
  • Adapts to new situations
  • Scales by choosing the right model
  • Costs depend on usage (tokens)
The original Transformer architecture paper — "Attention Is All You Need" by Vaswani et al. (2017) — is what started all of this. Every major LLM today is built on this foundation.

Interactive Exercise

Question 1 of 5

A Zapier workflow that sends a Slack message when a form is submitted is:

Key Takeaway

AI exists on a spectrum. Most businesses need a mix of simple automation (for predictable tasks) and AI agents (for tasks requiring judgment). Knowing the difference saves you from overpaying for "AI" that's just a Zapier workflow with a chatbot skin.

Action item: Audit your current tools. Which ones are truly AI vs. basic automation? Are you paying AI prices for automation-level features?

Watch & Learn More

But what is a GPT? Visual intro to Transformers

3Blue1Brown

How Large Language Models Work

IBM Technology

References

[1] Vaswani, A. et al., “Attention Is All You Need” (2017). Link

[2] Stanford HAI, “AI Index Report 2025” (2025). Link

Download the PDF Summary

Get the key takeaways, decision frameworks, and action items as a reference document.

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