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Practice/Embeddings

Embedding Map

Click two words on the map. Closer dots are a stand-in for similar meaning — the same idea behind search, RAG, and recommendations.

Click two words. Distance on this map is a teaching stand-in for how embedding models place similar meanings near each other. It is not a real OpenAI or Voyage vector.

kingqueenprinceParisFranceTokyoJapancatdogkittenprompttokenmodel

Word A

king

Word B

queen

Map distance

10.8

Very close — these ideas sit in the same neighborhood. Same-color dots are a cluster we placed by hand so the idea is visible.

Why this map exists

Meaning as coordinates

An embedding is a list of numbers that places a word or sentence in a space. Nearby points tend to share meaning.

Search without keywords

“refund after two weeks” can still match a note titled “returns” if they sit near each other in that space.

This map is a sketch

Real models use hundreds or thousands of dimensions. Two dimensions are just so you can see clusters with your eyes.

Further reading

What are embeddings?

The full vector-search explainer.

Embedding examples

Concrete cases beyond this toy map.

Embedding models to know

Open and closed options teams actually pick.

Try the RAG Playground →

See those neighbors get stuffed into a prompt.

Frequently asked questions

Are these real model embeddings?+

No. Positions were placed by hand so royalty, cities, animals, and AI jargon form visible clusters. A production embedding model would compute the coordinates from data.

Why not 3D?+

Even 3D is still a cartoon of a 768- or 3072-dimensional space. Two dimensions is enough to feel “near vs far.”

Is anything I click saved?+

No. The map is static and runs only in your browser.

More practice tools

Tokenizer Playground

Type anything and watch it split into tokens live — the units LLMs actually read and get billed for.

Context Window Visualizer

See how much of a model's context window your text fills up, and what happens when you run out of room.

FROG in a Bowl Prompt Builder

Fill in Format, Role, Objective, Goal, and Context — get a copy-ready structured prompt in seconds.

Machine Learning Types

Three tiny games show what supervised, unsupervised, and reinforcement learning actually mean.

Neural Network Playground

Drag two sliders and watch a real, tiny neural network turn them into a decision, live.

Generative AI Playground

Watch AI write one word at a time by predicting what's most likely to come next.

RAG Playground

Ask a question, watch notes get retrieved, then see a grounded answer versus a hallucination.

Attention Visualizer

Click a word and see which others a toy transformer looks at — including the classic “it” puzzle.

Prompt Injection Lab

Watch a pasted email try to override a support agent, then flip a switch that treats it as data.