LoreFable Editorial6 min read
What Is a Vector Database?
A fable about a cartographer who learned that closeness can matter more than alphabetical order.
vector database
5 articles
These are the core ideas behind every AI product. Each one is explained in plain English with a short fable. Start here if you are new to AI.
A neural network trained on massive text to predict and generate language. LLMs learn statistical patterns from billions of sentences, not facts from a database.
Read more →RAG gives AI a way to look up relevant documents before answering. Instead of relying only on memorized knowledge, it retrieves fresh evidence and uses it to write a grounded response.
Read more →Embeddings translate text into numbers so that similar meanings produce similar coordinates. This lets AI find related content by closeness in meaning, not just keyword matching.
Read more →A vector database stores embeddings and enables fast similarity search. It is the storage layer behind RAG, semantic search, and recommendation systems.
Read more →The context window is how much text an AI can read in a single request. Once full, older content gets dropped to make room for new — like a whiteboard that wipes when it fills up.
Read more →A fable about a cartographer who learned that closeness can matter more than alphabetical order.
A simple story about retrieval-augmented generation, grounded answers, and why evidence matters.
A fable about large language models, how they learn from text, and why scale changes everything.
A story about AI embeddings, meaning as coordinates, and why proximity is the point.
A fable about AI memory, context windows, and how systems remember across conversations.
