What is Embedding?
Turkish: Embedding
An embedding represents text, images, products, or other data as numeric vectors that can be compared for semantic similarity.
What is an Embedding?
An embedding represents text, images, audio, products, or other data as a numeric vector. Items with similar meaning or characteristics are placed closer together in the vector space.
How Does It Work?
An embedding model takes an input and returns a fixed-length array of numbers. For example, “where is my tracking number?” and “when will my order arrive?” use different words, but in a customer support context they may produce similar vectors because the intent is related.
Those vectors are compared with cosine similarity, dot product, or similar measures. This allows semantic search instead of only exact keyword matching.
Common Uses
- Semantic search and document retrieval
- Retrieving relevant context for RAG-based AI assistants
- Product recommendation and similar item matching
- Grouping customer messages by topic or intent
- Preprocessing for image or text classification
Business Use
Embeddings are common in NLP and generative AI systems because they create a search layer between raw business data and the model. Vectors are often stored in a vector database, where the nearest records can be found at query time.
Good results depend on more than model choice. Data cleanup, chunking strategy, refresh frequency, metadata filters, and false-match testing should be designed together.
Related Terms
Chunking splits long text into meaningful, manageable passages that search and RAG systems can retrieve accurately.
NLP (Natural Language Processing)NLP is the AI field that processes human language as text or speech for tasks such as classification, search, summarization, and generation.
Recommendation EngineA recommendation engine ranks products, content, or actions for each user based on behavior, item features, and context.
RerankingReranking re-scores an initial result set with a stronger model so the most relevant documents move to the top reliably.
Semantic SearchSemantic search finds relevant results by comparing the meaning of queries and content, not only matching exact keywords.
Vector DatabaseA vector database stores embeddings and retrieves records by semantic similarity, making it a core layer in AI search systems.
Vector Index (pgvector)A vector index speeds up similarity search by organizing embeddings so nearest neighbors can be found efficiently at scale.