How to build an internal AI knowledge base
Build an internal AI knowledge base from governed company documents, with a practical blueprint for ownership, retrieval, permissions, testing and upkeep.
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RAG & knowledge bases
Build, prepare and evaluate knowledge systems that retrieve the right source material with clear security and access boundaries.
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RAG & knowledge bases guides and articles.
Every published resource filed under RAG & knowledge bases. The foundational guide appears first where one applies.
8 articles
RAG pipeline architecture: components, stages and decisions
Understand RAG pipeline architecture from source preparation and indexing through retrieval, generation, evaluation, refresh and control points.
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How to prepare documents for RAG
Prepare documents for RAG with a practical checklist for selection, permissions, parsing, metadata, versioning, chunking, testing and ingestion.
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RAG chunking strategies: how to choose and test them
Choose and test RAG chunking strategies using document structure, metadata, retrieval quality and task-specific evaluation instead of fixed defaults.
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How to build a local RAG system
Build a local RAG system and compare local, self-hosted, on-premises and cloud choices without treating deployment location as a security guarantee.
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RAG access control: preserving permissions through retrieval
Design RAG access control that preserves source permissions through indexing, retrieval and generation, with practical patterns and failure checks.
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RAG security: risks, controls and a practical threat model
Threat-model a RAG system across documents, indexing, retrieval and generation, then apply layered controls for permissions, provenance and testing.
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RAG evaluation: metrics, test sets and release gates
Evaluate a RAG system with test sets, retrieval and answer metrics, security tests, release gates and regression monitoring tied to real user tasks.
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