Category:
Category:
Embeddings
Category:
Core AI & LLM Concepts
Definition
Vector representations of text that capture semantic meaning.
Explanation
Embeddings convert words, sentences, or documents into high-dimensional vectors that encode meaning. They power semantic search, clustering, recommendation engines, retrieval systems, agent memory, and classification workflows. Embedding quality defines RAG accuracy, vector search precision, and agent grounding reliability.
Technical Architecture
Text → Embedding Model → Vector → Store/Retrieve → LLM
Core Component
Embedding model, dimensionality, distance metric, normalization
Use Cases
Search, RAG, clustering, anomaly detection, personalization, routing
Pitfalls
Bad embeddings → irrelevant retrieval; low semantic fidelity; domain mismatch
LLM Keywords
Embeddings, Vector Representation, Semantic Vectors
Related Concepts
Related Frameworks
• Vector DB
• Semantic Search
• Reranking
• Embedding Evaluation Matrix