AI Hallucinations – Definition, Causes & How to Avoid Them

What are AI hallucinations?

AI hallucinations refer to the generation of content by artificial intelligence systems that may appear plausible, but is in fact objectively incorrect, unfounded, or misleading. This phenomenon is particularly common in large language models (LLMs), which are trained to generate natural-sounding text. Since these models calculate probabilities rather than “understand” information in the traditional sense, they can produce distortions or entirely fabricated facts.

In professional and business contexts, AI hallucinations pose a significant challenge, as incorrect information can have serious consequences for decision-making, automation processes, and the public perception of companies.

Causes of AI hallucinations

The causes of AI hallucinations are diverse and can be traced to technical, data-related, and application-specific factors:

1. Insufficient or faulty training data
   • Outdated, incomplete, or contradictory data: AI models rely on large datasets, which are not always up to date or accurate.
   • Lack of ground-truth data: Without verified and validated reference data, the risk increases that a model will generate incorrect content.
2. Lack of context or imprecise inputs
   • Ambiguous or vague user queries can lead to hallucinations, as the model attempts to generate plausible responses based on limited input.
   • Misinterpreted intent: If the model does not understand what exactly is being asked, it may produce an irrelevant or fabricated answer.
3. Technical limitations of large language models
   • Statistical probabilities instead of factual knowledge: Language models do not “think” logically; they generate text based on statistical likelihoods.
   • No access to real-time data: AI models without access to current information sources may rely on outdated or unverifiable knowledge.

Types of AI hallucinations

AI hallucinations can manifest in various ways:

• Internal contradictions: The AI generates statements within a single text that contradict each other.
• Misinterpretation of the prompt: The model’s response does not match the original question or includes additional, unrelated content.
• Factual errors: The AI fabricates numbers, data, statistics, or events.
• Context-free hallucinations: The generated content bears no relation to the actual query.

Risks and consequences of AI hallucinations

AI hallucinations can have significant implications:

• Spread of misinformation: Users may accept AI-generated content as true and disseminate it further.
• Reputational damage for organisations: Companies relying on LLMs risk losing trust if they publish incorrect information.
• Legal ramifications: In fields such as medicine, law or finance, inaccurate AI responses can lead to compliance issues or liability risks.

Detecting and preventing AI hallucinations

1. Human validation (“Expert in the Loop”)
   A proven approach is to integrate domain experts into AI-driven processes. Before content is published or used, it is manually reviewed by qualified personnel.
2. Verification using external knowledge bases
   Connecting language models to structured, verified data sources – such as encyclopaedias, scientific articles, or corporate knowledge – enables more accurate, evidence-based outputs.
3. Use of AI-optimised rules and filters
   By implementing programmatic rules, it can be ensured that the AI does not generate content outside its validated knowledge domain.
4. Transparency and user education
   AI-generated content should always be clearly labelled, allowing users to critically assess the credibility of the information.

With Retresco, AI hallucinations can be minimised through the use of structured, domain-specific knowledge bases and rule-based models. Our AI-powered content generation relies on reliable, context-aware data sources and content pools to deliver accurate, fact-based and trustworthy information.

Sources and PDFs

Harvard Business Review – "How Businesses Can Manage AI Risks"

Forbes – "AI Hallucinations: How Can Businesses Mitigate Their Impact?"

Stanford HAI – "Hallucinating Law: Legal Mistakes with Large Language Models Are Pervasive"

OpenAI Developer Community – "Strategies for Preventing Hallucinations in Responses Generated by the Assistant API"