AiVibeAI Hallucination
An AI hallucination is output from a generative model that is fluent and plausible but factually incorrect, unsupported by its sources or invented outright, such as a fabricated citation, specification, procedure or statistic.
Language models generate a probable continuation of text rather than retrieving verified facts, so gaps in training data, ambiguous prompts or questions beyond the model's knowledge can lead to confident but false answers. Hallucinations include invented references, wrong numbers, misattributed quotations and incorrect code or API usage. Vision-language models can also describe objects that are not present in an image.
In engineering and industrial settings, hallucinated parameter values, alarm meanings or procedures could lead to wrong decisions or unsafe actions, which is why outputs affecting equipment, safety or compliance must be verified against authoritative documentation.
Mitigations include grounding answers with retrieval-augmented generation and citations, instructing models to say when they do not know, constraining outputs to structured formats, automated faithfulness checks, evaluation on domain test sets and human review of consequential outputs. No current technique eliminates hallucination entirely, so system design must assume it can occur.
Key points
- Plausible but false or unsupported output from a generative model
- Arises because models predict likely text rather than verified facts
- Grounding, citations and evaluation reduce the risk
- No current technique eliminates hallucination entirely
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.