Model hallucination history

Overview. Hallucination describes generated content that is nonsensical or unfaithful to its source despite appearing fluent and natural.

Context. Retrieval can reduce hallucination without eliminating it: retrieval-based legal research tools still hallucinated 17% to 33% of the time in one test.

Overview. Hallucination describes generated content that is unsupported or false despite being presented fluently.

Context. Retrieval, citations, constraints, and verification can reduce risk, but none makes every generated claim trustworthy by default.

Overview. Hallucination describes generated content that is unsupported or false despite being presented fluently.

Context. Retrieval, citations, constraints, and verification can reduce risk, but none makes every generated claim trustworthy by default.

How to use this failure mode entry for Model hallucination. Connect the definition to a concrete data flow: identify the inputs, transformation, outputs, objective, and evaluation that make the concept observable in a real system.

Evidence note. This Concepts failure mode record for Model hallucination is presented as an as-of-2026-07-13 orientation. The claim ledger identifies the source supporting its core statements; follow that record for the publication's full context and any later changes.

Hallucination describes generated content that is unsupported or false despite being presented fluently.

Retrieval, citations, constraints, and verification can reduce risk, but none makes every generated claim trustworthy by default.

Why it matters: Model hallucination is best understood as part of a system, not as a label in isolation. The practical questions are what inputs it accepts, what outputs it produces, which evidence supports its capabilities, what constraints surround deployment, and how those details change over time. The Compendium separates the documented facts above from interpretation and records an as-of date so readers can re-check the primary source.

Reading note: this is original explanatory writing. It links to the cited source instead of reproducing protected text. Product availability, model names, pricing, and policies can change; follow the source for the provider's current position.

Hallucination describes generated content that is unsupported or false despite being presented fluently.

Retrieval, citations, constraints, and verification can reduce risk, but none makes every generated claim trustworthy by default.

Why it matters: Model hallucination is best understood as part of a system, not as a label in isolation. The practical questions are what inputs it accepts, what outputs it produces, which evidence supports its capabilities, what constraints surround deployment, and how those details change over time. The Atlas separates the documented facts above from interpretation and records an as-of date so readers can re-check the primary source.

Reading note: this is original explanatory writing. It links to the cited source instead of reproducing protected text. Product availability, model names, pricing, and policies can change; follow the source for the provider's current position.