AI hallucination

AI hallucination is a phenomenon where an AI system, such as a large language model (LLM) or computer vision tool, produces outputs that are inaccurate, nonsensical, or not based on real-world data. These outputs appear plausible but are essentially “made up” by the AI because the system misinterprets patterns or lacks proper grounding in its training data.

AI hallucinations can occur for several reasons.

• Incomplete or biased training data: AI may hallucinate when trained on unrepresentative or flawed datasets, and data augmentation can help reduce this risk.
• Model complexity: Highly complex models can generate outputs that are not grounded in reality, but this can be mitigated through fine-tuning.
• Decoding and inference errors: Errors during inference or misinterpretation of prompts can cause AI hallucinations.
• Data poisoning: Maliciously altered or corrupted training data can make AI models produce inaccurate or misleading outputs.

To address AI hallucinations, developers can monitor inference results and continually refine the model, while retrieval-augmented generation (RAG), context engineering, and Chain of Thought (CoT) prompting can help ground the model in accurate information and improve output reliability.