A story of soup, feedback loops, and a million micro-decisions

What happens when a model "learns"?

It tastes the world one prediction at a time and slowly adjusts its recipe toward understanding.

🔁 Taste. Adjust. Repeat.

Imagine this: You're in the kitchen. A pot of soup simmers on the stove.

The recipe? Just a guide.

The real magic starts with a spoon…

You taste.

Too bland. You add a pinch of salt. Stir. Taste again.

Better but still flat. You add some cream.

Stir. Taste. Closer. Brighter. Still not perfect. A touch of pepper?

That slow, intuitive process of tasting and adjusting?

That's how an AI learns.

AI Training, Demystified

We tend to imagine AI learning as cold and abstract.

But it's actually a loop of trial and correction, repeated millions of times.

Let's break it down into 3 steps and a "secret sauce".

🥄 1. The first taste (Prediction)

The model makes a guess. Maybe it predicts the next word in a sentence. Or labels an image.

It's like saying:

"I think the soup is ready?" It's rarely right the first time.

🤔 2. The "Needs Salt" Moment (Loss Function)

The model receives feedback. Not just “wrong”, but how wrong.

This is the loss function.

It measures the distance between the model's guess and the correct answer.

"This tastes like a 6. We're aiming for a 10. You're 4 points off."

🧂 3. The pinch of salt (Optimizer)

Using that feedback, the model adjusts its internal parameters (just slightly).

It doesn't throw the whole salt shaker in.

It nudges itself in a better direction.

Then it tries again.

Taste. Adjust. Repeat.

Gradient Descent: The AI Chef's Secret Sauce ✨

That tiny improvement step is called Gradient Descent

the optimizer most models use to get better over time.

It sounds technical. It's really not.

It's the logic of any good cook:

"If it needs salt, add a little. Taste again."

Imagine walking down a foggy hill, you can't see the bottom,

but you feel which direction slopes downward.

You take a step. Then another.

Eventually, you reach the valley, the optimal flavor.

Your brain has an Optimizer, too

Great chefs don't always measure. They taste. They adjust.

Years of feedback have trained their palate.

They've optimized it, not with formulas,

but with intuition built from thousands of meals.

Your brain does this.

You've been gradient descending your whole life.

Why this matters?

We often think of AI learning as magic, genius, or a complex process. But it's more humble than that.

It's not a flash of brilliance.

It's patience. Refinement. Feedback. Iteration.

The only real difference?

The AI doesn't have to wait for the soup to cool.

It has a million spoons, and it learns in microseconds.

Learning (human or artificial) always starts with a mistake. Perhaps intelligence isn't knowing; it's just adjusting.