Retail executives hear “Generative AI” and imagine something mysterious: a machine that thinks. In reality, it's much simpler. At its core, Generative AI — like ChatGPT — is a supercharged next-word prediction software.
Think of it like this:
- When you type a text on your phone, it suggests the next word (“Happy… Birthday”).
- Generative AI does the same thing — except instead of choosing between three options, it predicts from 50,000+ possible words at every step, billions of times over.
It's not “thinking” like a human — it's pattern recognition at scale.
The Two Steps to Building an LLM
Andrej Karpathy, one of AI's most respected teachers, breaks it down into two steps that any retailer can understand:
Step 1: Pre-training (Learning the Language)
Imagine training a new cashier. You give them millions of old receipts and ask:
“Guess the next item in the basket.”
At first, they're terrible. But after billions of guesses across thousands of receipts, they start to see patterns:
- “If someone buys shampoo, conditioner often follows.”
- “If someone buys beer on Friday, chips are likely next.”
That's what training an AI is like: feed it huge amounts of text, ask it to guess the next word, and fine it when it's wrong. Over time, it becomes excellent at predicting sequences — not just groceries, but words, sentences, ideas.
This is Step 1: build a model that is very, very good at predicting what comes next in language.
Step 2: Fine-tuning (Specializing for the Job)
Now imagine that same cashier has learned the basics — scanning items and spotting patterns — but still needs training for your store.
You teach them your discount rules, how to process returns, and how to greet customers the way you want.
That's what fine-tuning does for AI. After pre-training, the model is exposed to curated examples and human feedback so it can:
- Answer questions safely and helpfully.
- Write content that fits a specific tone.
- Follow instructions in a way that feels natural.
This is Step 2: take a general prediction engine and specialize it so it becomes useful, safe, and aligned for real-world tasks.
Why This Matters for Retail Leaders
Here's the key insight: Generative AI isn't magic — it's math.
It doesn't “understand” in the human sense. It predicts with uncanny accuracy because it has seen patterns at internet scale. The leap comes from how versatile language is: once you can predict text, you can answer questions, draft marketing copy, even analyze financials.
For retailers, this means:
- Better Customer Engagement: AI can predict what words or offers resonate with shoppers.
- Smarter Operations: AI can summarize mountains of sales data into plain English.
- Faster Innovation: AI can generate new ideas (campaigns, product descriptions, training manuals) in minutes, not weeks.
Final Thought
Generative AI is like a seasoned store manager who's seen every receipt, every customer complaint, and every sales report ever written.
They don't “think” — but they've memorized patterns so deeply that their predictions feel like intelligence.
And that's the big unlock: by mastering prediction, AI gives retailers a new tool for decision-making, creativity, and speed.