Knowledge Base
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DataAbout 1 min read
A dynamic data repository that stores an enterprise's internal data in a searchable and reason-capable structure, allowing AI to find information in real time and use it as a basis for answers.
Also known as
KBKnowledge BaseAI Knowledge Base
Detailed explanation
A knowledge base is the system that forms the foundation of Retrieval-Augmented Generation (RAG) technology, converting and managing various source data such as text, PDFs, and databases into a vector format that AI can understand. Unlike a simple repository that just pools files together, it identifies the intent behind a user's question to extract the most relevant information within milliseconds and passes this to an LLM (Large Language Model) to guide it toward accurate answers free of hallucinations. Modern systems go beyond simple search to support hybrid search (keyword + semantic) and agentic retrieval to handle complex business logic. This is recognized as core infrastructure that allows corporate sensitive data to be safely reflected in AI services without directly training it into the models.
Why It Matters in Tool Selection
General-purpose AI models are not aware of an enterprise's latest internal policies or undisclosed projects. A knowledge base fills this information gap and serves as a key indicator of AI service reliability by clearly providing citations for its answers.
What to Look For
- Real-time data synchronization (Sync) and indexing speed
- The variety of supported data source connectors (S3, SharePoint, SQL, web crawling, etc.)
- Whether it supports hybrid search combining lexical (keyword) and semantic search
- The ability to control data access in compliance with existing internal security policies (ACL)
Example
When operating a customer service chatbot, if you connect the latest 'refund policy PDF' and 'product specifications' to the knowledge base, the AI can answer based on the exact policy just like an agent and provide page numbers of the document as references.
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