7 Approaches to Building AI Knowledge Bases, from Vectors to Live Updates
goyalshaliniuk · x · 2026-10-14
Shalini Goyal outlines 7 approaches to building AI knowledge bases, arguing that connecting documents to an LLM is far from the only option:
- Vector-based: embeddings + semantic retrieval for RAG (Qdrant/Weaviate/Milvus)
- Knowledge graphs: entity-relation modeling for multi-hop questions (Neo4j/GraphRAG)
- Hybrid search: semantic + keyword for enterprise search (Elasticsearch/OpenSearch)
- Structured data: relational tables/JSON for precise queries and calculations (PostgreSQL/MySQL)
- Document-centric for document-heavy knowledge
- Hierarchical for large organized collections
- Continuously updated: pipelines that detect new/modified docs, reprocess, refresh embeddings, remove stale info, and track versions
Related event: Seven Ways to Build AI Knowledge Bases Beyond Vector Search(5 posts)→
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