Knowledge base: definition

A knowledge base is the curated collection of documents an AI system draws on to answer questions, and keeping it accurate and current is what keeps the answers accurate and current.

In a RAG system, the knowledge base is the source of truth the model retrieves from: your docs, policies, FAQs, and past tickets, organised so the right piece can be found.

Its quality sets the ceiling on the whole system. A tidy, current knowledge base gives good answers; a stale, messy one gives confident wrong ones no matter how good the model is.

Related terms

Retrieval-augmented generation (RAG): Retrieval-augmented generation is a technique that fetches relevant passages from your own documents at question time and hands them to the model, so its answer is grounded in your data instead of its training.
Embeddings: An embedding is a list of numbers that captures the meaning of a piece of text, so that passages about similar things sit close together and can be found by similarity rather than exact words.
Vector database: A vector database stores embeddings and finds the closest matches to a query vector quickly, which is what lets a RAG system retrieve the right passages from millions of documents in milliseconds.