AI/ML
Embeddings
What embeddings are and why they matter
Definition
Embeddings are dense numerical vectors that represent the semantic meaning of data - text, images, or other content - in a high-dimensional space. Items with similar meanings have similar embedding vectors, enabling AI applications to perform semantic search, clustering, recommendations, and classification based on meaning rather than exact keyword matching.
How it works
Think of embeddings as coordinates in a meaning space. The word "dog" and "puppy" would be close together. "Dog" and "spacecraft" would be far apart. But instead of two or three dimensions, embeddings typically have hundreds or thousands of dimensions, allowing them to capture subtle relationships between concepts.
Embedding models (like OpenAI's text-embedding-3 or open-source alternatives like BGE and E5) convert text into these vectors. The quality of the embedding model directly impacts downstream performance. A better embedding model means better search results, better recommendations, and better RAG pipeline accuracy.
In practice, embeddings are generated once for your content (at index time) and once for each query (at search time). The computational cost is at index time - you process your entire dataset through the embedding model. Query-time embedding is fast because it processes only the user's input. This makes embedding-based search highly performant even at scale.
How 1Raft uses Embeddings
We use embeddings as the foundation of every semantic search and RAG system we build. We benchmark multiple embedding models against our client's actual data before choosing one. In a media project, switching from a generic embedding model to one fine-tuned for the domain improved retrieval accuracy by 18%, which directly improved the quality of AI-generated answers.
Related terms
AI/ML
Vector Database
A vector database is a specialized database designed to store and search high-dimensional numerical representations (embeddings) of data. It enables fast similarity search, which is the foundation of AI-powered search, recommendation systems, and RAG pipelines.
AI/ML
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that combines a language model with a searchable knowledge base. Instead of relying solely on what the model learned during training, RAG retrieves relevant documents first, then generates answers grounded in that specific data.
AI/ML
Natural Language Processing (NLP)
Natural language processing is the branch of AI focused on enabling machines to understand, interpret, and generate human language. It covers everything from sentiment analysis and text classification to machine translation and conversational AI.
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
AI/ML
Transformer Architecture
The transformer is the neural network architecture behind virtually all modern language models. Introduced in 2017, it uses a mechanism called self-attention to process entire sequences of text in parallel, making it far more efficient and capable than previous approaches.
Related services
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