Cosine Similarity in Vector AI: Geometry, Not Confidence
Work through cosine similarity, normalization, dot products, distance, and edge cases. Learn why a similarity score is not a confidence probability.
VECTORTOKEN LAB / CATEGORY
Foundations
Get the language right before you build.
Tokens, token IDs, contextual representations, and passage embeddings answer different questions. This collection starts at those boundaries and follows them into the geometry used by retrieval systems. It is designed for readers who need to review an architecture, understand a model output, or explain why two arrays should not be compared just because their dimensions match.
Begin with the vector token introduction, then follow the contextual embedding walkthrough. Use the cosine similarity article when you need to inspect normalization, sorting direction, or a threshold. Try its small numerical examples by hand before diagnosing a large search system. The suggested reading order moves from vocabulary to representation to comparison, keeping the represented unit explicit at every stage.
A useful outcome is a written representation contract: the model and tokenizer revisions, the input role, the output unit, pooling, normalization, and the intended metric. Carry that contract into ingestion and search instead of relying on an undocumented field named embedding.
THE READING PATH
03 articles to explore.
Token Vectors: From Lookup Tables to Contextual Embeddings
Follow token IDs into contextual representations. Understand pooling, representation contracts, and the checks that make embeddings reproducible.
Vector Token Explained: Tokens, IDs, and Embeddings
Separate tokens, vocabulary IDs, and embeddings. Build a precise vocabulary for vector AI, model inputs, storage, and semantic retrieval.