RAG & Retrieval
Knowledge Base
A structured repository of information that an AI system can query to answer questions or provide context.
In depth
A structured repository of information that an AI system can query to answer questions or provide context. In AI applications, knowledge bases are often backed by vector databases and used in RAG pipelines, letting models access up-to-date, domain-specific facts that were not part of their training data.
Example
In practice, developers reach for Knowledge Base when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover RAG & Retrieval.
FAQ
Common questions
What is Knowledge Base?
A structured repository of information that an AI system can query to answer questions or provide context.
Why does Knowledge Base matter for AI developers?
Knowledge Base sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Knowledge Base?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Knowledge Base. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
RAG & Retrieval
Knowledge Cutoff
The date after which a model has no training data.
RAG & Retrieval
OWASP Top 10 for LLM Applications
The OWASP Top 10 for LLM Applications is a ranked list of the most critical security risks in apps built on large language models, published by the OWASP Gen AI Security Project.
RAG & Retrieval
Context Recall
A RAG evaluation metric that measures how much of the information needed to answer a question was actually retrieved.
Put this concept to work
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