Vector Embeddings Explained to My Mom

Mom: “So, what is this vector thing you keep talking about? And what’s an embedding? Sounds like math soup!”

Alright mom, let me break it down.

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1. Think of Words as Flavors

Imagine you have a big spice cabinet. You’ve got cinnamon, pepper, sugar, salt, and so on. Each spice has its own taste — sweet, salty, spicy, or bitter.
Now, if you wanted to describe a spice, you might give it a “flavor score” for each taste:

• Cinnamon: (Sweet: 8, Spicy: 2, Salty: 0, Bitter: 0)

• Pepper: (Sweet: 0, Spicy: 9, Salty: 0, Bitter: 1)

That list of numbers is like a vector — it’s just a way to represent something using numbers.

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2. How This Works for Computers

For a computer, words are just letters — it doesn’t understand meaning.
So we give each word its own “flavor profile” (vector), but instead of sweet/spicy/salty, we use hidden mathematical features that capture meaning:

• “Cat” might be close to “Dog” but far from “Car.”

• “Apple” (fruit) will be closer to “Banana” than “Laptop.”

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3. Why It’s Called an Embedding

When we “embed” something, we place it inside a space. In this case, we embed words into a space of numbers. This space is special — it’s designed so that similar meanings end up close together.
If we plotted them on a map:

• “King” and “Queen” would be near each other.

• “Paris” would be close to “France.”

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4. Why It’s Useful

• Search engines can find relevant results even if you don’t use the exact words.

• Chatbots (like me!) can understand context better.

• Recommendation systems can suggest movies or products similar to the ones you like.

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Mom: “So basically, vector embeddings are just giving words or things a ‘taste test’ in number form, so the computer knows which ones are similar?”
Me: Exactly, mom! You nailed it.