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RAG and knowledge bases: avoiding confident but wrong answers

AI quality follows data quality. A short guide to document structure, updates, and response checks before they reach customers.

Retrieval-Augmented Generation pulls relevant chunks from PDFs, Notion, or Confluence into the prompt. Without structure and freshness, the model may sound confident while being wrong.

Good practices: meaningful chunking, explicit version dates, refusal when confidence is low, and human review for sensitive topics.

Our technology section shows the stack we use for such builds. News articles support the main site with expert content—easier to target long-tail queries and route visitors to services and contact.

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