RS Glossary
Embedding
An embedding is a numerical representation of an item—such as text, an image, or audio—constructed so that items with relevant similarities can be compared mathematically.
- Review state
- Needs review
- Freshness
- Current
- Version
- 2
- Embedding
- Vector Embedding
Remember it this wayEmbeddings turn content into coordinates for comparison.
In plain language
An embedding converts content into a list of numbers. A system can then search for nearby representations instead of relying only on exact keyword matches.
Why it matters
Embeddings support semantic search, retrieval, recommendations, clustering, and other features, but their notion of similarity reflects the model and data used to create them.
Example
A knowledge-base search uses embeddings to find a policy passage about travel expenses even though the employee searched for reimbursement rather than the exact words in the document.
Common misconception
An embedding is not a human-readable summary and mathematical closeness does not prove two items mean the same thing in the decision context.
Related terms
Sources
- Google for Developers, Machine Learning Glossarytechnical definition of embeddings and vector representations.
Learn more
- See how embeddings support [[retrieval-augmented-generation|Retrieval-Augmented Generation]].
Public history
36 recorded events · 36 public summaries
A potential shared concept was discussed and selected for initial glossary drafting.
The captured candidate received a complete initial draft and entered section-level human review.
Radar reviewed the initial definition draft for human consideration.
Radar reviewed the initial plain-language-explanation draft for human consideration.
Radar reviewed the initial why-it-matters draft for human consideration.
Radar reviewed the initial example draft for human consideration.
Radar reviewed the initial common-misconception draft for human consideration.
Radar reviewed the initial remember-it-this-way draft for human consideration.
Radar reviewed the initial related-terms draft for human consideration.
Radar reviewed the initial sources draft for human consideration.
Radar reviewed the initial learn-more draft for human consideration.
A proposal was opened for section-level human review while preserving the current version.
A human reviewer opened a proposed revision while preserving the current approved or captured version.
definition approved by a human reviewer.
plain-language-explanation approved by a human reviewer.
why-it-matters approved by a human reviewer.
example approved by a human reviewer.
common-misconception approved by a human reviewer.
remember-it-this-way approved by a human reviewer.
related-terms approved by a human reviewer.
sources returned for revision.
learn-more approved by a human reviewer.
Requested wording was prepared without changing approved or existing sections.
Radar reviewed the pending sources section for human consideration.
Radar must rereview the complete embedding term after an accepted text change.
Radar must rereview the complete embedding term after an accepted text change.
Radar must rereview the complete embedding term after an accepted text change.
Radar must rereview the complete embedding term after an accepted text change.
Radar must rereview the complete embedding term after an accepted text change.
Radar must rereview the complete embedding term after an accepted text change.
Radar must rereview the complete embedding term after an accepted text change.
Radar must rereview the complete embedding term after an accepted text change.
Radar must rereview the complete embedding term after an accepted text change.
sources approved by a human reviewer.
sources approved wording became the current section text.
All section gates are approved; content version 2 is now a fully approved term.
Source: vault/Glossary/terms/embedding/term.md · sha256:b9d6501787545d611e81fa7b72de1aa87f7c57ca8977a9bcdf0d5cd392ee7b04 · Release: rs-glossary-glossary-team-poc-0.5.5-2026-09-02