Vector Database
A database optimized for storing and querying high-dimensional embeddings used in AI applications.
A vector database stores high-dimensional embedding vectors (typically 384-3072 dimensions) and retrieves them via approximate nearest-neighbor (ANN) search. Vector databases are the foundation of RAG, semantic search, recommendation systems, and personalization. Leading vector databases include Pinecone, Weaviate, Qdrant, Milvus, and pgvector (Postgres extension). Selection criteria include scale (millions vs billions of vectors), latency targets, hybrid search support (combining vector + keyword), metadata filtering, and managed-vs-self-hosted operational tradeoffs. Empire325 designs vector database architectures for retrieval applications including chunking strategy, embedding model selection, and reranker pipelines.
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Large Language Model (LLM)
A neural network trained on massive text corpora to understand and generate human language.
Retrieval-Augmented Generation (RAG)
An AI architecture combining LLM generation with real-time retrieval from external knowledge sources.
AI Agent
An autonomous LLM-based system that plans, takes actions via tools, and accomplishes multi-step goals.
Fine-Tuning
Adapting a pretrained foundation model to specific tasks or domains via additional training.