Seven Ways to Build AI Knowledge Bases Beyond Vector Search

On October 14, goyalshaliniuk published a thread series (with infographics) systematically covering 7 approaches for connecting knowledge to LLMs. The core point: "connecting documents to an LLM" is not the only path — the choice should depend on data shape and query needs.

Confirmed

Vector knowledge base: Documents are converted to embeddings and stored in a vector database, retrieved by semantic similarity rather than keywords; well-suited for document search and RAG, commonly using Qdrant, Weaviate, Milvus
Structured data knowledge base: Information is stored in relational tables, JSON, or databases that AI queries directly for fields and records; suited to scenarios like customer info, product catalogs, finance, and inventory that require exact values, filtering, and aggregation, commonly using PostgreSQL/MySQL
Knowledge graph: Organizes information as entities and relationships, ideal for expressing relations between facts
Hybrid search knowledge base: Combines semantic retrieval with exact matching
Document-centric approach: Suited to document-heavy knowledge
Hierarchical approach: Suited to large, well-ordered collections
Continuous update pipeline: Suited to frequently changing information

Why it matters

The author offers a quick reference: vector → semantic similarity; knowledge graph → factual relationships; hybrid search → semantic + exact matching; structured data → precise queries and computation
The author stresses these approaches are not mutually exclusive — production systems often combine them, e.g., hybrid search layered over structured queries, so engineering choices should start from requirements rather than defaulting to vector retrieval

2026-10-14 ~ 2026-10-14 · 5 related posts

Primary sources

7 approaches to building AI knowledge bases, from vector RAG to continuously updated pipelines — goyalshaliniuk · 2026-10-14 14:28 (UTC+08:00)
7 Approaches to Building AI Knowledge Bases, from Vectors to Live Updates — goyalshaliniuk · 2026-10-14 14:28 (UTC+08:00)

[source] 7 Approaches to Building AI Knowledge Bases, from Vectors to Live Updates — goyalshaliniuk · 2026-10-14
[source] 7 approaches to building AI knowledge bases, from vector RAG to continuously updated pipelines — goyalshaliniuk · 2026-10-14
Vector Databases vs Knowledge Graphs: Picking the Right AI Knowledge Base — goyalshaliniuk · 2026-10-14
Beyond RAG: Structured Data and Hybrid Search for AI Knowledge Bases — goyalshaliniuk · 2026-10-14
Production AI Systems Should Combine Multiple Knowledge Base Approaches — goyalshaliniuk · 2026-10-14