Most developers skip embeddings because they “sound maths-y”.

Embeddings are literally the thing that makes AI useful beyond chat.
They power:

• RAG
• semantic search
• ranking
• deduplication
• memory
• document QA
• clustering
• anomaly detection

If LLMs are the brain, embeddings are the eyes and ears.

🧠 What embeddings actually are

An embedding is just:

a list of numbers describing meaning.

A sentence goes into a model → you get a vector like:

[0.123, -0.441, 0.982, ...]

Two vectors pointing in similar directions = similar meaning.

📐 Cosine similarity

Cosine similarity is just:

how close the angle is between two vectors.

1.0 = identical
0.0 = unrelated
-1.0 = opposite

🔍 Why this matters

Embeddings turn messy text into queryable math.

Suddenly, you can:

• find similar errors
• match CVs to jobs
• cluster products
• detect duplicates
• build memory systems
• power semantic search

🏗️ How to use embeddings

1. Pick a model
2. Store vectors (pgvector, LanceDB, etc.)
3. Query by cosine similarity
4. Feed matches back to an LLM

🐸 GhostFrog example

Embeddings will help cluster products, group listings, detect patterns, and eventually build flipping memory.