Learning path · Embeddings & Representation · 36
Vector Similarity
Scoring how close two embeddings are—usually cosine similarity or dot product—to rank candidates for retrieval.
Why it matters
- Similarity threshold choices control precision-recall tradeoffs.
- Normalization affects comparability across indexes.
- Hybrid search combines similarity with lexical scores.
Key ideas
- Cosine similarity
- Dot product
- Distance metrics
Top resources
- 01DocsPinecone
Understanding embeddings
Why this resource. Cosine vs dot product in language you can explain to a stakeholder.
Covers in this concept
- cosine
- dot product
- nearest neighbor
- 02DocsOpenAI
Embeddings guide
Why this resource. Which distance the API’s embeddings are designed for.
Covers in this concept
- normalization
- similarity
Vector similarity turns geometry into ranking. Cosine similarity ignores magnitude—good when embeddings are normalized. Dot product favors longer vectors—common in some ANN libraries. Calibrate thresholds on labeled query sets; a single global cutoff rarely works across product areas. ".
Updated 2026-08-09 · Full learning path