Course

Embeddings & Semantic Search

Understand embeddings as semantic vectors — token-to-text chain, cosine similarity, chunking, and identifier blind spots.

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Modules

3 total

Explain the chain from token IDs → token embeddings → pooled text embeddings.

Explain cosine top-k as the retriever's ranking signal and why similarity ≠ relevance.