Knowledge Base
A structured or semi-structured collection of information used by AI systems to retrieve factual data. In the context of RAG, it typically refers to the document collection that the system can search.
Why It Matters
A well-organized knowledge base is the foundation of effective RAG systems. The quality of your knowledge base directly determines the quality of AI-generated answers.
Example
A company's internal wiki, documentation site, and FAQ database serving as the knowledge base that an AI chatbot searches to answer employee questions.
Think of it like...
Like a well-organized library — the books (knowledge) are only useful if they are cataloged, searchable, and accessible when someone needs information.
Related Terms
Retrieval-Augmented Generation
A technique that enhances LLM outputs by first retrieving relevant information from external knowledge sources and then using that information as context for generation. RAG combines the power of search with the fluency of language models.
Vector Database
A specialized database designed to store, index, and search high-dimensional vector embeddings efficiently. It enables fast similarity searches across millions or billions of vectors.
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords. It uses embeddings to find results that are conceptually related even if they use different words.
Knowledge Graph
A structured representation of real-world entities and the relationships between them, stored as a network of nodes (entities) and edges (relationships). Knowledge graphs capture factual information in a machine-readable format.