AI Knowledge Base Architecture
Designing scalable AI knowledge base architectures.
Cassandra Research — AI Division
Research methodology: Validated against peer-reviewed AI research, NIST frameworks, and industry benchmarks.
Overview
An AI knowledge base is a structured repository of authoritative information designed to be queried by AI systems. For professional services, knowledge bases contain legislation, case law, rulings, clinical guidelines, and other domain-specific content that must be accurate and current.
Architecture Components
- •Content ingestion and processing pipeline
- •Document chunking and structuring strategy
- •Embedding model selection and vectorisation
- •Vector database for semantic retrieval
- •Metadata taxonomy and filtering system
- •Citation and provenance tracking
Design Principles
Effective AI knowledge bases prioritise content authority (only verified sources), retrieval precision (finding the most relevant information), currency (keeping content up to date), and traceability (maintaining clear citations back to original sources).
Frequently Asked Questions
How large should an AI knowledge base be?
Size depends on the domain. Legal knowledge bases may contain hundreds of thousands of documents. Quality and relevance are more important than sheer volume.
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