Vector Embeddings Explained to My Mom

When I first told my mom I was working with “vector embeddings,” she paused, looked over her glasses, and said,

“Is this something to do with your school geometry? Or some kind of stitching?”

Not exactly, Ma. Let me explain in a way that makes sense — no computer science degree required.

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Imagine your handwritten recipe notebook

Like many Indian homes, we have that old notebook filled with recipes — some learned from nani, some from TV chefs, some WhatsApp forwards.

If I asked you to find similar recipes, you wouldn’t just look for the exact same name. You’d group them based on what they are:

• “Gajar ka halwa” and “Suji ka halwa” belong together — both are desserts cooked with ghee and sugar.

• “Masoor dal” and “Moong dal” go in the same group — both are lentil curries, though the taste and texture differ.

You’re grouping by meaning, not exact words.

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Computers can’t taste, but they can embed

A vector embedding is simply a list of numbers — like GPS coordinates — that represents the meaning of something: a recipe, a sentence, even a photo.

For example:

• “Gajar ka halwa” → [0.12, 0.88, -0.31, 0.05, ...]

• “Suji ka halwa” → [0.11, 0.87, -0.29, 0.04, ...]

If these lists of numbers (vectors) are close together in “meaning space,” it means they are conceptually related — even if the words are different.

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Why it matters

1. Smarter search
Instead of just matching words, embeddings let search engines understand meaning.
Search for “biryani” and it can also suggest “pulao” — because they’re similar rice-based dishes, even if the names differ.

2. Better recommendations
When YouTube suggests a video that “feels” like the one you just watched, embeddings are probably at work. They compare your choice with millions of others in meaning space.

3. AI understanding
Embeddings help AI compare ideas, so it can respond in a way that actually makes sense — not just repeat words back.

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A GPS for ideas

Here’s how I told Ma:

Think of every recipe, song, or photo having a location pin on a special map. But instead of roads, this map shows “meaning.” If two pins are close, they’re related. The computer just measures that distance to find matches.

She nodded thoughtfully… then asked if the “meaning GPS” could find her the best gulab jamun recipe.