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“Answer based solely on the context.”

Is that really sufficient safeguards for internal AI assistants? In many RAG (Retrieval-Augmented Generation) projects, control is almost entirely delegated to the system prompt.

The expectation: a precisely worded prompt prevents hallucinations and ensures that outdated documents are ignored.

In practice, this quickly proves to be a fallacy.

A language model is not a deterministic software programme. When, within the internal database, an informal Teams chat from yesterday comes up against an official quality management guideline from the previous year, it is not chance or a simple ‘new overturns old’ principle that decides – but rather a clear hierarchy based on:

  • Validity
  • Binding nature (document authority)
  • Scope
  • Timeliness

In our new technical guide, we examine how a robust architecture for enterprise RAG systems must be structured: from prompt caching at API level to automated quality assurance.

Download the full white paper

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