Confabulation
An alternative term for hallucination, used in the United States National Institute of Standards and Technology Generative AI Profile (NIST AI 600-1, 2024) to name confidently stated but erroneous or fabricated content. Preferred by some researchers because "hallucination" borrows a clinical term for perceiving things that are not present, and NIST's own framing cautions that terms like "hallucination" risk implying the system has perceptions or intentions it does not have. Either word names the same behavior; neither implies the model is aware of what it is doing.
Defined in 2 GAGE programs, which carry 4 distinct definitions of it. The wording above is taught in AI Governance: Applied Mastery.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
An alternative term for hallucination, used in the United States National Institute of Standards and Technology Generative AI Profile (NIST AI 600-1, 2024) to name confidently stated but erroneous or fabricated content. Preferred by some researchers because "hallucination" borrows a clinical term for perceiving things that are not present, and NIST's own framing cautions that terms like "hallucination" risk implying the system has perceptions or intentions it does not have. Either word names the same behavior; neither implies the model is aware of what it is doing.
the technically precise term the NIST Generative AI Profile (AI 600-1) uses for what is colloquially called AI "hallucination," a generative AI system producing fluent but factually incorrect or fabricated output.
The Generative AI Profile's term for a generative model producing confident, fluent output that is false, commonly called hallucination. Named as one of the twelve generative-AI risk categories because a confident false output can be more dangerous than an obvious error.
The established tendency of a generative AI model, including one that retrieves documents or calls tools on your behalf, to produce confident, plausible, false output; the reason every AI-surfaced red-team finding is a lead to verify, not a verdict (NIST AI 600-1, Generative AI Profile, 2024).
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- What a model cannot know: hallucination produced on demand, then caught · Build Before You Govern, AI Governance: Applied Mastery
- NIST AI RMF as an operating system: mapping your organization onto govern, map, measure, manage · The World's Rulebooks, AI Governance: Applied Mastery
- Your conformity file under attack: the red team finds what you missed · Adversarial Governance, AI Governance: Applied Mastery
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.