The Factory with a New Motor
We bolted the motor on. Let's see what it can do.
How the Machine Works
A large language model is a next-token prediction machine:
Tokenization
Text is broken into small pieces (“tokens”, roughly 3/4 of a word).
Pattern Recognition
Trained on massive text, it learned statistical patterns about which tokens follow others.
Probability
Given a sequence, it predicts the most likely next token. Repeats. That's it.
Emergent Capabilities
At scale, surprising abilities arise: reasoning, coding, analysis. It doesn’t “know” - but the results can be remarkable.
See It in Action
Watch how tokens flow through the model - from input to prediction, one token at a time.
Why AI “Forgets” & Can't Go Back
Stateless
Every conversation starts from zero. The AI has no memory of yesterday's chat. It's not being difficult - it literally doesn't know.
Autoregressive
Tokens are generated left-to-right, one at a time. It can't revise earlier tokens once written. That's why your feedback matters - the AI literally can't go back and fix it on its own.
This is not a flaw - it's the architecture. Pattern matching at scale, not reasoning. The results can be brilliant, but it's statistics all the way down.
The Full Pipeline
From your prompt to the AI's response - this is what happens under the hood.
Not All Models Are Equal
Rule of thumb: bigger ≠ better. Match the model to the task.
| Model | Context | Price / 1M tokens | Best For |
|---|---|---|---|
| Claude Opus 4.6 | 1M | $5.00 | Complex analysis, strategy |
| Claude Sonnet 4.6 | 1M | $3.00 | Day-to-day work, coding |
| GPT-5.4 | 272K–1M | $2.50 | Complex reasoning |
| Gemini 3 Pro | 1M | ~$2.00 | Huge document analysis |
| Llama 4 Scout | 10M | $0.12–$0.88 | Privacy, open source |
| DeepSeek R1 | 64K | $0.55 | Budget reasoning |
Watch out for thinking overhead: Reasoning models burn tokens on simple tasks - thinking thinking thinking… “hello.” An “expensive” model isn’t always better. Match complexity to the task.
The #1 Rule: Context Is Everything
Don't rely on AI “knowing” - it doesn't. Research first. Always. Ground it in reality.
Bad
“Summarize this project”
Good
“You are reviewing a pharma supply chain audit for a Belgian food producer. Here is the full report: [...]”
The difference between a useless answer and a brilliant one is context.
The “New Hire on First Day” Analogy
You wouldn't dump every company document on a new hire's desk and say “make me an invoice.” You'd extract the relevant info and write a clear brief. Treat AI the same way. This connects directly to the instruction documents section later.
Developer Productivity: GitHub Copilot
Faster task completion
Faster PR cycle
Code written by AI
AI code kept in final version
90% of Fortune 100 companies use Copilot. 20 million users by mid-2025. Caveat: AI-assisted code can increase issue counts (~1.7x) if not paired with proper review. Speed without governance = faster mistakes.
Sources: GitHub Research 2022 · GitHub × Accenture 2024 · GitHub Economic Impact 2023 · CodeRabbit Report 2025
GitHub Copilot in action
Writing a function with Copilot autocomplete, tab-accepting suggestions, and inline chat to refactor code.
Generating Images
| Tool | Best For |
|---|---|
| Midjourney | Best aesthetics/art |
| GPT Image | Easiest, best prompt adherence |
| Adobe Firefly | Safest commercially (licensed data) |
| Stable Diffusion | Full control, open source |
| FLUX | Photorealistic, fast |
Note: Claude does NOT generate images - it excels at image analysis/vision only.
The Ghibli Story (March 2025)
GPT-4o went viral with Studio Ghibli-style images. Miyazaki had called AI animation “an insult to life itself.” Sam Altman: “our GPUs are melting.” Major copyright debate - style is NOT explicitly protected by copyright.
Image generation
Generating an image with GPT and/or Midjourney, comparing outputs and styles.
Gamma & Presentation Tools
Gamma.app - AI-powered presentations. Agent researches, generates, and restyles entire decks via conversation. Good for quick first drafts - always review and refine.
Gamma presentation
Creating a presentation from a single prompt in Gamma, then restyling it via conversation.
Markdown: Why and How
Universal & Portable
Every AI tool reads/writes it natively. Works in any editor, converts to HTML/PDF/slides.
Structured
Headings, lists, tables, code blocks - AI understands the hierarchy.
Version-friendly
Plain text = easy to diff, track changes, and collaborate.
Lightweight
Tiny files compared to .docx or .pptx - easy to share and store.
Quick syntax
# Heading 1 ## Heading 2 - Bullet point **Bold** and *italic* | Table | Header | [Link](url)
Google AI Studio
Google AI Studio vibe coding
Describing a simple CRM app → AI generates it → working app → deploy to Cloud Run or Netlify. All free, no code.
Data stays local: Google AI Studio creates the app with AI, but your actual data (CSVs, spreadsheets) is processed in the generated app on your device - not sent to the AI model.
Better context = better apps. Just like prompting, the more specific your description, the better the result. Research first, describe clearly.
Ask AI to Help You Ask Better Questions
When you need advice from an expert, you don't walk in and ask random questions. You ask someone to help you figure out what to ask.
The same applies to AI. Before asking a complex question, ask the AI: “What should I ask you to get the best answer about X?”
Direct (often weak)
“How do I improve my team's productivity?”
Meta-question (better)
“I manage a 12-person finance team. What questions should I ask you to get actionable advice on improving our quarterly close process?”
This is a teaser - in Phase 2, we'll turn this into a full meta-prompting workflow.
Meta-questioning demo
Asking AI 'what should I ask you?' and comparing the quality of answers to direct questions.
Instruction Documents & Custom Instructions
“You wouldn't hire someone and explain the job differently every morning.”
Claude: Projects with custom instructions / system prompts
ChatGPT: Custom GPTs / “Customize ChatGPT” instructions
Google AI Studio: Persistent system instructions
Think of it as an onboarding document for your AI. The better the document, the better every interaction.
Write your first instruction document
- 1
Open Claude (claude.ai) or ChatGPT (chatgpt.com) on your laptop/phone
- 2
Pick a task you do repeatedly at work (e.g. meeting summary, email reply, report)
- 3
Ask the AI to write you a reusable instruction prompt:
Write me a reusable instruction prompt for [your task]. Include placeholders for the parts that change each time. - 4
Test it: paste in real (or example) content and run the prompt
- 5
Save it somewhere you'll find it tomorrow
So... is this enough?
We bolted on the motor. Things go faster. But look at the numbers again: 88% of companies are here. Only 6% are seeing real impact.
“The factory still has the same belts, the same layout, the same bottlenecks. The email still sits in the mailbox for a day.”