AI hallucinations

February 27, 2026
|By Julia Schönau

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Hallucinations in AI applications occur when generative AI models produce content that does not exist or is factually incorrect. A classic example: a language model names Sydney as the capital of Australia instead of the correct answer, Canberra. 

Such errors arise because LLMs are based on probabilities rather than striving for truth. They always calculate the next probable word and can thus also generate fictitious sources, non-existent studies, or erroneous biographies. 

Particularly problematic: AI hallucinations often appear very convincing and are difficult for laypeople to detect. In critical areas of application such as medicine, law, or even customer service, such errors can have serious consequences.

Why are hallucinations so relevant in AI?

For companies, hallucinations in customer-oriented AI agents such as chatbots or voicebots pose a significant risk. Incorrect information can damage trust in the brand, and inaccurate information about prices, availability, or terms and conditions can have legal consequences. 

The causes of hallucinations are manifold: 

  • incorrect training data

  • outdated knowledge

  • technical limitations of the model architecture 

  • unclear user input

Studies show that even modern LLMs (depending on the task and model) have error rates ranging from 2.5 to almost 80 percent. Companies must therefore take active measures to reduce hallucinations and ensure the quality of their AI systems.

Hallucinations in practice

Preventing hallucinations requires a multi-layered approach. Technically proven methods include retrieval augmented generation (RAG), which connects LLMs to verified knowledge databases, and guardrails—protective mechanisms that detect and block inappropriate outputs in real time. 

In addition, high-quality, diverse training data, clear prompt engineering techniques, and regular testing help.

BOTfriends specifically relies on RAG technology and company-specific knowledge bases in its conversational AI solutions. knowledge basesto ensure reliable answers. If no reliable information is available, the system communicates this transparently instead of speculating. 

This ensures reliable customer communication and strengthens trust in AI-supported dialogues. Companies should also train their employees to critically review AI outputs and maintain human control in critical processes.

Frequently Asked Questions (FAQ)

AI hallucinations arise from several factors: faulty or incomplete training data, outdated knowledge, technical weaknesses in the model architecture, and the statistical functioning of LLMs. These models calculate probable word sequences without checking for truth. Unclear prompts or excessively high random parameters (temperature) during generation can also promote hallucinations.

No, AI hallucinations cannot be completely ruled out at this time. Even modern models have error rates. However, these can be significantly reduced through technologies such as RAG, guardrails, high-quality training data, and targeted prompt engineering. BOTfriends combines these approaches to achieve maximum reliability in corporate communications.

AI hallucinations can be identified by fact-checking with reliable sources, plausibility checks, and critical questioning of the output. Technically, uncertainty measures, self-consistency tests (multiple answers to the same question), and automated fact-checking with knowledge databases can help. Users should pay particular attention to numbers, data, names, and source references, as hallucinations often occur here.

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