Embeddings confused me for years.
Until I finally understood the real mental model.

🧠 1. Words become coordinates in high-dimensional space

A model converts text into a vector like:

[0.12, -0.43, 1.77, ...]

Not encryption.
Not compression.
Not magic.

Just meaning encoded as numbers.

🧭 2. Distance = similarity

Two texts with similar meaning end up close together.

Example: - “PlayStation 5” - “PS5 console” - “Sony PS5 disc edition”

All live in the same neighbourhood.

This is why embeddings power: - RAG
- classification
- semantic search
- similarity detection

🔍 3. Searching becomes geometric

Instead of searching for keywords, you search for nearby vectors.

That’s why vector DBs (Pinecone, Chroma, etc.) exist.

🧩 4. Why embeddings matter for developers

They’re the foundation of:

• document chat
• retrieval systems
• product matching (GhostFrog uses this pattern conceptually)
• category detection
• recommendation engines

Once you “get” embeddings, half of modern AI architecture suddenly makes sense.

🚀 5. And the best part?

You don’t need to understand the maths.

You only need the mental model.

That’s what this post is for.