Understanding Vector Embeddings and Positional Encoding

Arun ChauhanArun Chauhan
4 min read

Why These Concepts Matter ?

When you send a message to a chatbot, ask an AI to translate a sentence, or search the web, something fascinating happens behind the scenes. The AI isn’t “reading” words the way you do — it’s converting them into numbers and patterns it can process.

Two of the most important tricks that make this possible are called vector embeddings and positional encoding.

  • Vector embeddings : helps the AI understand what a word means

  • Positional encoding : helps AI remember where that word belongs in a sentence

Think of it like giving the AI both a dictionary and a map. Without these, it wouldn’t be able to make sense of language in the way we expect.

In this article, we’ll break these concepts down into simple terms, skip the heavy math, and use easy analogies so you can see exactly how they work and why they matter so much in modern AI.

The Big Picture — What Problem Were They Solving?

Before modern AI language models, many systems read sentences word by word in a fixed order, like reading a book aloud. That made them:

  • Slow for long sentences

  • Bad at remembering earlier parts of a text

  • Limited in how they handled context

Newer models changed the game by letting the AI look at all words in a sentence at the same time — but to do that, they needed a smart way to:

  1. Turn words into numbers that computers can understand (vector embeddings).

  2. Keep track of where each word is in a sentence (positional encoding).

What Are Vector Embeddings?

(a) The Challenge: Computer can’t understand words

Computers work with numbers, but language is made of words. To process text, we need a way to represent words as numbers without losing their meaning.

(b) The Idea: Represent Words as Points in Space

Imagine a giant 3D map — but instead of cities, each point represents a word. Words that are related are placed closer together on the map:

  • “Cat” and “Dog” might be neighbours

  • “Cat” and “Banana” would be far apart

In reality, this “map” isn’t 3D — it might be hundreds of dimensions. But the idea is the same: distance and direction capture meaning.

Embeddings are the first step, each word is converted into its vector form before the model does anything else. Think of it as translating language into “AI’s native format” so the rest of the model can work its magic.

What Is Positional Encoding?

(a) The Challenge: AI Models Don’t Naturally Understand Order

Some AI architectures process all words in a sentence at the same time.
That’s great for speed and flexibility — but it also means they have no built-in idea of order.

If we give it

“The cat chased the dog” and “The dog chased the cat”

Without extra help, both will be same for the AI.

(b) The Idea: Give Each Word a Sense of Position

Positional encoding adds a sort of “GPS coordinate” to each word vector that tells the model where it is in the sentence.

  • Vector embedding → the flavour of each ingredient in a recipe (sweet, salty, spicy — captures meaning).

  • Positional encoding → the order in which the ingredients are added (affects the final dish’s meaning).

Without embeddings, you don’t know what flavours you have.
Without positional encoding, you dump ingredients randomly and ruin the dish.

Now the model can tell the difference between the same word appearing early or late in a sentence.

Why This Matters in Real Life

Vector embeddings and positional encoding might sound abstract, but they power the AI tools you see every day:

  • Google Translate: Understands meaning across languages.

  • Chatbots: Keeps conversations in order and relevant.

  • Search engines: Finds results based on meaning, not just keyword matching.

Without these techniques, AI language tools would either:

  • Confuse word order and meaning

  • Struggle to connect related concepts

ConceptWhat It DoesWhy It Matters
Vector EmbeddingsTurn words into numbers that capture meaningLets AI understand meaning
Positional EncodingAdd location info to word vectorsKeeps sentences in the right order
TogetherMeaning + OrderEnables true language understanding
0
Subscribe to my newsletter

Read articles from Arun Chauhan directly inside your inbox. Subscribe to the newsletter, and don't miss out.

Written by

Arun Chauhan
Arun Chauhan