Explaining AI Vector Embeddings to My Indian Mom (Without Any Science Jargon)

When I told my mom I was writing about AI vector embeddings, she gave me that look the same one she gives when I start talking about coding or “cloud” that doesn’t involve actual rain.

She never studied science, so words like “vector” or “embedding” mean nothing to her. But she’s a master at organizing her kitchen, handling wedding seating plans, and remembering who’s who in Bollywood and that’s exactly how I explained it.

Embeddings in Simple Words

Embeddings are AI’s way of remembering the meaning of things and knowing how close or far those meanings are from each other.

Think of it as an invisible meaning map every word, picture, or sound gets a position on this map.

• Closer = more similar in meaning

• Farther = less related

Now, let’s go through four examples my mom instantly understood.

1. The Masala Dabba Map

In every Indian kitchen, there’s a masala dabba (spice box):

• Haldi (turmeric) and mirchi (chili) are next to each other both are spices.

• Doodh (milk) and tel (oil) are together both are liquids.

How it connects to embeddings:
Just like the spice box groups similar flavors together, AI groups similar meanings together. In embedding space, haldi and mirchi have “similar numbers,” so they’re placed near each other.

2. The Wedding Seating Chart

At a wedding:

• Your side of the family sits together

• The groom’s side sits together

• Kids play in one corner

How it connects to embeddings:
AI doesn’t have a physical seating chart, but embeddings work like one in its “mind.” Similar things are seated (placed) close together, and strangers are far apart.

3. The Bollywood Genre Map

If I say Shah Rukh Khan, you think romance.
If I say Akshay Kumar, you think comedy or action.
If I say Lata Mangeshkar, you think songs.

How it connects to embeddings:
AI also places similar actors close together two romantic actors will be near each other in its meaning map, while a singer will be in a different cluster.

4. The Dog–Cat–Pedigree–Milk Distance Story

Here’s where “distances” get interesting:

• Dog and Cat are close together (both pets).

• Pedigree and Milk are close together (both foods, but for different eaters).

Now, if we go from Dog → Pedigree, and then from Pedigree → Milk, the length of that journey in AI’s meaning space can be similar to going Dog → Cat.

Why this matters:
It means AI can compare relationships across categories. For example:

• Dog is to Pedigree as Cat is to Milk

• Or in mumma’s kitchen terms: Haldi is to Curry as Tea Leaves are to Chai

Why Embeddings Are Powerful

Embeddings let AI:

• Search: If you look for chai masala, it shows elaichi and dalchini.

• Recommend: If you liked one romantic film, it suggests another.

• Understand: If you say mitha while talking about chai, it guesses sugar; if during Diwali shopping, it guesses sweets.

The Final Takeaway

If I had to explain embeddings to my mom in one line:

Embeddings are AI’s way of placing everything on a giant invisible map so it knows what’s close, what’s far, and how those distances compare just like you organize your spices, wedding guests, and Bollywood stars in your mind.

And the best part? AI does this for everything words, images, sounds, recipes, even emojis.