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Explain Like I'm 5 /AI Concepts

What is an Embedding?

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AI models operate through mathematical logic. Any data that an AI model operates on, including unstructured data such as text, audio or images, must be expressed numerically. Vector embedding is a way to convert an unstructured data point into an array of numbers that still expresses that data’s original meaning.
— Dave Bergmann/ Senior Writer, AI Models, IBM
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The first two sentences about artwork and last two sentences that share the keyword dogs are nearer to one another than the first and third sentences, which share no common words or meanings.

Embeddings are a way of turning human language — like words, sentences, or even full documents — into numbers that a machine can understand.

But not just any numbers. The numbers are designed so that similar meanings result in similar vectors. For example, 'king' and 'queen' might be close together in this space, and the relationship between 'man' and 'woman' is encoded as a direction.

These vectors live in what's called a high-dimensional space — often with hundreds or thousands of dimensions. You can’t visualize it easily, but conceptually, it's like mapping language into a giant 3D galaxy where meaning determines position.

How They’re Created:

  • A model like Word2Vec, BERT, or OpenAI’s embedding models is trained to predict context or next words.
  • It learns to represent words and phrases as vectors that capture meaning, usage, and relationships.

Key Features:

  • Semantic Similarity: Close vectors mean related meanings
  • Dense & Efficient: Captures meaning in a compact numerical form
  • Flexible: Can be applied to text, code, images, or audio

Real-World Use Cases:

  • Search Engines: Matching your query with semantically similar results
  • Recommendation Systems: Suggesting content based on similarity in vector space
  • RAG Systems: Retrieving the most relevant documents to answer a prompt
  • Clustering & Classification: Grouping similar items automatically

You can think of an embedding as a brain’s way of “feeling out” what something means, not just what it says.

FAQ

What are embeddings in AI?
Embeddings are how we turn things like words or sentences into numbers that a model can understand. Each word gets mapped to a list of numbers — like coordinates in a big space — and words with similar meanings end up close together. So 'cat' and 'kitten' will land near each other, while 'cat' and 'car' won’t.
Why do models like ChatGPT need embeddings?
Models don’t understand text like we do — they need numbers. Embeddings are the first step in turning text into something math-based. They're what help the model 'get' that 'dog' and 'puppy' are related, or that 'weather' and 'climate' are in the same ballpark.
Embeddings vs. one-hot encoding
One-hot encoding is like giving every word its own nametag — it doesn’t say anything about how words are related. Embeddings, on the other hand, are learned from data and actually capture meaning. They understand that 'happy' and 'joyful' are similar, while one-hot just treats them as totally separate things.
Are embeddings just for text?
Nope. Embeddings can be used for images, audio, code, users — pretty much anything. For example, an image can be turned into an embedding so you can search for 'shoes like this one'. Same idea: turn it into numbers, then compare.
How are embeddings created?
Embeddings are learned during training. Some models like Word2Vec or GloVe learn them by looking at which words appear near each other. In modern LLMs, embeddings are part of the model itself — the first layer turns each token into a vector, which then gets refined as it moves through the network.
What is semantic similarity in AI?
It means two things have similar meaning. If 'coffee' and 'espresso' show up in similar places in text, their embeddings will be close together. You can measure that closeness with math — like checking the angle between the two vectors.
Are all embeddings the same?
No. They can differ in size (how many numbers are in the vector) and in how good they are. Bigger isn’t always better — it depends on how the embeddings were trained and what you're using them for.

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