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Definition
An embedding is a dense vector produced by a model that maps content into a space where distance corresponds to semantic similarity.
Simple explanation
Imagine putting every sentence on a giant invisible map, where sentences that mean the same thing end up near each other. An embedding is that map coordinate.
Why it matters
Embeddings are the currency of modern AI infrastructure — used for search, clustering, classification, recommendations and de-duplication.
How it works
- 1ModelAn embedding model converts input to a vector of hundreds or thousands of numbers.
- 2CompareSimilarity is measured by cosine distance.
- 3UseSearch, cluster, classify or filter by that similarity.
Real examples
Products named for illustration only. Inclusion is not an endorsement.
- OpenAI text-embedding-3Popular general-purpose embedding model.
- Cohere Embed v3Multilingual embeddings tuned for retrieval.
Advantages
- Enable meaning-based comparison.
- Compact and fast to compute.
- Work across languages and modalities.
Limitations
- Different models produce incompatible vectors.
- Domain shifts can hurt quality.
Common misunderstandings
- ClaimEmbeddings understand meaning like humans.RealityThey approximate meaning based on training data patterns.
Frequently asked questions
Can I mix embeddings from different models?
No — always compare within the same model.
How big is an embedding?
Commonly 384, 768, 1024 or 1536 dimensions.
The Tool Money Lab perspective
Once you understand embeddings, most 'AI-powered' features stop being magic — they become search, comparison and grouping.
Conclusion
Embeddings turn language into geometry. That single trick powers most of the AI features you use every day.