Chapter C · 8 pages · ~119 min
What are embeddings?
Understand how embedding models turn meaning into useful numerical neighborhoods, then design and evaluate semantic search systems with realistic caveats.
What you will be able to do
- Explain embeddings as learned numerical representations without assigning human-readable meanings to individual dimensions
- Compare cosine similarity, dot product, and Euclidean distance using concrete examples
- Trace a semantic search request from chunking and embedding through ranking and result inspection
- Diagnose quality failures caused by data, bias, domain, language, and retrieval design
- Choose an embedding model, similarity metric, index, and evaluation plan for a practical workload
- Connect embeddings to vectors, vector databases, and retrieval-augmented generation