RAG Implementeren: Hoe Je AI Laat Werken met Bedrijfseigen Data
Praktische implementatie van Retrieval-Augmented Generation (RAG). Van documentupload tot vector search - zonder hallucinaties.
Retrieval-Augmented Generation (RAG) grounds answers in your documents - when chunking, embeddings, and access control are done deliberately. This note focuses on implementation choices that prevent silent drift.
Chunking and metadata
Smaller chunks improve precision but lose context. Enrich chunks with source, section, and sensitivity labels so retrieval respects policy.
Evaluation loops
Measure groundedness and citation coverage weekly on a fixed eval set. Regression in retrieval quality is cheaper to fix before users notice.
Security boundaries
Vector stores inherit file permissions. If search ignores ACLs, RAG becomes a bypass for confidential data.
Ship RAG like any data product: with metrics, owners, and rollback.
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