Zero to AI engineer · Stop 10 of 14 lessons

Embeddings, without the maths

What this lesson covers

Embeddings are the substrate of retrieval, of similarity search, of half of the RAG stack. They’re also frequently the wrong tool. This lesson makes both true statements make sense.

The outline

1. A vector as coordinates in meaning-space. The mental model that works without a linear-algebra prereq.
2. Cosine similarity, in one line of Python. Why it dominates over Euclidean and dot product for text.
3. Turning text into a vector. The provider APIs, choosing a model, and why you should never mix embedding models mid-corpus.
4. What embeddings capture well. Topic, tone, paraphrase, translation.
5. What they capture badly. Negation, dates, numbers, entities — the things retrieval quietly gets wrong.
6. Dimensionality and cost. 384 vs 1536 vs 3072 — the tradeoff between quality, storage, and query latency.

Coming soon

In outline.