Where this shows up

Embeddings are the least flashy idea in AI and quietly one of the most used. Turn anything into a vector where closeness means similarity, and a huge range of products become a nearest-neighbor lookup. You've been using embeddings all day without seeing them.

Here's where "meaning as a place in space" pays off.

The same idea, many jobs

All of these reduce to embed-then-compare:

• Search and recommendations rank everything by cosine similarity to a query or to what you liked (semantic search).
• RAG — the retrieval behind modern chatbots — embeds your question, finds the closest documents, and hands them to an LLM so it can answer from real sources.
• Multimodal search works because text and images can share one space (CLIP).
• Moderation and dedup treat "too similar to known-bad" as a signal.

That's the course

You built it from the ground up: a word becomes a vector, closeness means similarity, the vectors are learned from context, they support arithmetic, anything can be embedded, and nearest- neighbor lookup powers search and recommendations.

If you enjoyed this, the other courses show these embeddings at work — in recommender feeds, chatbots, and image models.