What are hallucinations?

In AI, a “hallucination” refers to a situation in which a model generates an incorrect or invented answer, even though it sounds linguistically correct and often very convincing. The term originally comes from medicine, but here it means: the AI “makes up” things that are not factually true.

This happens because models like GPT do not know what is “true” – they only calculate likely word sequences based on statistical patterns in their training data. The result is answers that appear trustworthy but can be factually completely wrong.

Why are hallucinations so risky?

Hallucinations are risky because AI systems can present false information in a highly convincing way, leading to bad decisions, incorrect commitments to customers, or compliance violations. In companies where processes, legal issues, and finances are affected, such errors can cause direct financial damage, loss of reputation, and legal consequences.

Well-known hallucinations

What are hallucinations in ChatGPT?

Models like ChatGPT are also susceptible to hallucinations, especially with complex, ambiguous, or very open questions. They can:

  • cite non-existent sources,
  • invent people or products,
  • or present factually incorrect relationships.

Example: A technical question about configuring a specific ERP system might be answered by ChatGPT with seemingly detailed but completely invented steps, because there is no real connection to the company’s internal documentation.

Causes

What causes hallucinations in AI?

There are several common causes:

  1. Missing or contradictory data in the training material
  2. Statistical text generation without an understanding of truth
  3. Unclear or overly open prompts
  4. Lack of up-to-date or domain-specific knowledge
  5. Overload due to contextual complexity
  6. Fundamental training configuration: an LLM will always try to give a helpful answer to a question.

Most generative AI models work purely probabilistically – and without targeted access to reliable sources, hallucinations are almost inevitable.

Possible solutions

Is the hallucination problem solvable?

It can at least be significantly reduced through targeted technical measures. Particularly effective are:

  • Retrieval-Augmented Generation (RAG): The AI accesses external knowledge sources instead of relying only on its training.
  • Model combinations: Using several specialized models in parallel reduces the error rate.
  • Semantic post-checks: Answers are checked and, if necessary, corrected before being displayed.
  • Well-structured knowledge bases: The foundation for reliable answers.

The octonomy system

How octonomy avoids hallucinations

octonomy goes beyond classic models: Octo-Worker do not only use generative algorithms, but work with a carefully structured, domain-specific knowledge base – the so-called Octo-Knowledge.

This knowledge base includes:

  • manuals, guidelines, and product information
  • technical specifications and legal documents
  • context-specific experience data from CRM, ERP, and other systems.

The key difference: Octo-Worker do not generate answers “from the gut feeling of the AI”, but deliver verified information based on your internal know-how. The result is human-level answers – without the risk.

With this combination of structured knowledge, semantic intelligence, and cross-model control, octonomy achieves a documented answer quality of over 95 % – with no hallucinations.