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How Can Enterprise AI Avoid Hallucinations? 6 Ways to Reduce Them

By NOVARK Editorial Team·Published ·Updated

AI hallucination is when a model produces content that sounds plausible but has no basis. Enterprises can reduce hallucinations by using retrieval-augmented generation (RAG) over their own documents, requiring answers to cite the document, page and paragraph, having the AI say clearly that it does not know when evidence is insufficient, checking prompts and imported data, limiting the scope of what the AI answers, and keeping human review for key decisions. Hallucinations cannot be eliminated entirely; the goal is to make them less frequent and make errors detectable.

Why does AI make things up?

Large language models generate text based on patterns in language, not by checking facts. When a question goes beyond what the model knows, when enterprise data does not provide enough evidence, or when the question itself is misleading, the model may still produce an answer that reads fluently but is wrong.

6 ways to reduce hallucinations

  1. Retrieval-augmented generation (RAG): first find the relevant passages in the enterprise knowledge base, then answer based on those passages.
  2. Cite sources: list the document, page and paragraph so users can check the original text.
  3. State clearly when evidence is insufficient: when no supporting evidence is found, say so directly rather than letting the model fill in the gaps.
  4. Check prompts and imported data: filter suspicious instructions and sanitise imported documents and web content.
  5. Limit the scope of answers: set which topics the AI can answer and which data it can access, according to its purpose.
  6. Human review: content involving decisions, external communication or compliance is confirmed by the responsible person.

How to verify the results

  • Build a test question set: collect common questions and reference answers, and test regularly.
  • Spot checks: sample real answers regularly to confirm the sources match correctly.
  • Collect feedback: let users report wrong answers, trace the causes and improve the data.

How NOVARK helps

NOVARK knowledge answers cite the document, page and paragraph so the original text is easy to check. When the evidence is insufficient, the AI says so clearly rather than filling in the gaps. Prompt checks and sanitisation of imported data further reduce the risk of improper instructions and wrong answers. Natural-language metric queries in Intelligent war room also show the data source and calculation method.

FAQ

Does using RAG eliminate hallucinations?

Not completely. RAG grounds answers in evidence, but if it retrieves wrong or outdated documents, or the model misreads the content, errors can still happen. That is why cited sources and human review are still needed.

How can I tell whether an AI answer is trustworthy?

First check whether it cites a source, then go back to the original text to confirm the content matches. Answers with no source, or whose source does not match the content, should not be used as they are.

This article is general guidance. Adapt the approach to your organisation's size, industry regulations and data. NOVARK is an enterprise AI agent platform.

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