Governance, Security & Trust

AI Hallucination

Adil MektoubAdil Mektoub

Published 13 July 2026Last reviewed 13 July 2026

Definition

AI Hallucination: is a confident but false or unsupported output produced by a language model — plausible-sounding text that is not grounded in fact — which is why enterprise AI must combine grounding, guardrails and human oversight.

Executive summary

Executive summary

Language models generate the most likely next words, not verified truth. When they lack grounding, they can produce fluent, confident statements that are simply wrong. This is inherent to how the technology works, not a bug to be fully patched.

Hallucination is managed, not eliminated: grounding answers in a Knowledge Base through RAG, constraining behaviour with guardrails, and keeping humans in the loop for consequential decisions all reduce its frequency and impact.

Key takeaways

Key takeaways
  • A hallucination is a confident but unsupported AI output.
  • It stems from how language models generate text.
  • Grounding with RAG substantially reduces it.
  • It is managed with oversight, not fully eliminated.

Architecture

Mitigation combines several layers:

  1. 1GroundingRetrieval from a knowledge base anchors answers in real content.
  2. 2ConstraintsGuardrails and prompts limit speculation and require sourcing.
  3. 3VerificationChecks and citations make answers auditable.
  4. 4Human oversightPeople validate consequential outputs before action.

Business example

Example implementation scenario

Asked about a contract clause without access to the document, a model might invent a plausible answer.

Grounded against the actual document through retrieval, it instead cites the real clause — or states it cannot find one.

FAQ

Frequently asked questions

Can AI hallucination be eliminated completely?
No. It can be substantially reduced through grounding, guardrails and verification, but not eliminated. This is why human oversight is retained for consequential decisions.
Does retrieval-augmented generation stop hallucination?
RAG greatly reduces it by grounding answers in real content, but a model can still misread or over-extend retrieved information, so oversight remains important.
Related concepts
Large Language Modelis an AI model trained on large volumes of text to understand and generate language, providing the reasoning and language ability at the core of an AI agent.Retrieval-Augmented Generationis a technique that grounds a language model's output in retrieved documents, so answers reflect trusted, current and permissioned data rather than the model's memory alone.Knowledge Baseis the curated, permissioned store of an organization's documents and data that an AI system retrieves from — through Retrieval-Augmented Generation — to answer questions and act on grounded, current facts rather than guesses.Human-in-the-Loopmeans a person reviews, approves or can override an AI system's consequential actions before they take effect, keeping accountability and judgement with humans.
Related GFO frameworks
GFO Enterprise AI Governance FrameworkGovernance Framework — is GFO's practical structure for governing enterprise AI — defining accountability, boundaries, approval workflows, data handling and audit so agentic systems stay trustworthy.
Adil Mektoub

Author

Adil Mektoub

Founder · Engineering & AI Infrastructure

France-based AI platform engineer. Age 36. E-mobility AI background; SAP and Vitol.

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