Managing AI hallucination
AI hallucination occurs when a language model produces an output that sounds fluent and confident but is actually incorrect or entirely fabricated. The critical point for enterprises is that this is not a temporary defect that the next model release will solve. Theoretical research and model providers’ own analyses indicate that hallucination is a structural risk that must be managed. For companies using and building AI, the real question is not “How do we eliminate it?” but “How do we manage it?”
The assumption that “the model is not mature yet; it will improve” is comforting because it postpones responsibility. In the meantime, the system continues to operate without sufficient control. A model does not verify reality; it generates statistically probable text. Fluency is not evidence of accuracy. Accepting that fact is the first step in managing hallucination.
In brief
- Xu, Jain, and Kankanhalli (2024) formally argue that hallucination cannot be eliminated completely: an LLM cannot learn every computable function and will therefore inevitably hallucinate when used as a general-purpose problem solver (arXiv:2401.11817).
- According to OpenAI’s September 2025 analysis, prevailing training and evaluation regimes penalize “I don’t know” and reward confident guessing, teaching models to bluff (OpenAI, 2025).
- The cost is concrete in high-risk fields. In a preregistered 2024 evaluation by Stanford and Yale researchers, legal AI tools that claimed to use RAG, Lexis+ AI and Westlaw AI-AR, hallucinated in 17–33% of queries, compared with 43% for general-purpose GPT-4 (Magesh et al., 2024).
- The solution is not to wait for a better model, but to build layered defenses: grounding, permission to abstain, verification, scope boundaries, and human control points.
Why is hallucination not simply a “bug that will be fixed”?
Hallucination arises from the way the model works. Xu, Jain, and Kankanhalli’s January 2024 paper approaches the issue formally. They define hallucination as a mismatch between a computable model and a computable “correct-answer” function, and use learning-theory results to argue that an LLM cannot learn every computable function. Used as a general-purpose problem solver, it will therefore inevitably produce hallucinations.
An important boundary should be stated clearly: this is a proof based on a formal definition of hallucination in a formal setting. It does not directly establish that every narrow practical system will fail on every task. The more measured enterprise conclusion is that no approach promising zero hallucinations should be trusted, and systems should be designed on the assumption that hallucination remains possible.
Current training regimes reinforce the tendency. According to OpenAI’s September 2025 explanation, common evaluation methods reward correct answers and penalize uncertainty, so models learn to guess rather than remain silent when unsure. Hallucination is therefore not only a side effect, but partly the result of the incentive structure.
Two kinds of hallucination: which one are you dealing with?
There are two main types of hallucination, and their defenses differ. The first is a factuality error: the output contradicts the external world and is objectively false. The second is a faithfulness error: the output contradicts or misrepresents its own source or input document.
The distinction is practical. Against factual errors, supplying correct and current information through grounding can help. Against faithfulness errors, supplying the source is not enough; each claim must be checked against the source. A team that does not know which type of hallucination it faces may apply the wrong remedy.
The legal-AI evaluation illustrates the distinction clearly. In Magesh and colleagues’ study, errors did not consist only of fabricated case names. They also included mischaracterizing real decisions and citing authorities irrelevant to the question. The latter can be much harder to detect than a completely invented citation.
Does grounding or RAG eliminate hallucination?
No. Grounding, anchoring an answer in retrieved source documents, can reduce hallucination substantially, but it does not eliminate it. The clearest evidence comes from independent testing of products marketed with claims that RAG solves the problem.
In a 2024 preregistered study by Stanford and Yale researchers, the providers’ claims were found to be overstated. Hallucination rates were lower than for general-purpose GPT-4, which recorded 43%, but RAG-based legal-research tools such as Lexis+ AI and Westlaw AI-Assisted Research still hallucinated in 17–33% of queries. Grounding is therefore a necessary layer, not a complete solution.
What does managing hallucination look like?
Management does not come from one technique, but from overlapping layers. Each layer closes a different portion of the risk; none is sufficient on its own.
| Layer | What it does | What it reduces |
|---|---|---|
| Grounding (RAG) | Anchors the answer in retrieved source documents | Fabrication when the model lacks knowledge |
| Abstention, the right to say “I don’t know” | Allows the model to withdraw when uncertain | Bluffing and confidently wrong answers |
| Verification and citation | Checks each claim against the source | Fake citations and source drift |
| Scope boundary | Keeps the model within a narrow, tested task | Fragility in ambiguous domains |
| Human control point | Requires human approval for high-risk output | Expensive errors reaching the outside world |
The common feature of these layers is that none requires a more powerful model. Each is a design decision around the existing model. This shifts hallucination from a procurement problem to an engineering and governance problem.
The abstention layer is especially underused. OpenAI’s analysis suggests that evaluation systems often penalize uncertainty. Enterprise systems can deliberately reverse that incentive. Telling the model “if you are not sure, do not claim an answer” and measuring whether it follows the instruction is one of the least expensive ways to reduce bluffing.
Is management enough, and where does the human remain?
Layered defenses reduce risk; they do not eliminate it. Since no technique fully removes hallucination, treating an output as reliable without human oversight is unsafe. In high-risk fields such as law, healthcare, and finance, a human control point is not optional.
The reason is observational, not merely theoretical. Hallucination rates reaching one in two or three queries in legal AI tools make human review essential. Some of the errors, such as mischaracterizing a real court decision, are subtle enough that a non-expert may not notice them. The value of automation lies not in removing verification, but in preparing the material that must be verified.
This aligns with GDP’s operating principle: AI prepares; people judge. The purpose of risk management is not to remove the person, but to place human judgment at the right point and equip it with the right information.
How does GDP build it?
- What we ground: We define which questions rely on which source set and how those sources are kept current.
- Where the system says “I don’t know”: We define the conditions under which the model must abstain and test those conditions through an evaluation set.
- Whose approval is required: We establish which outputs must be approved by which person before they leave the system.
Conclusion
Explaining enterprise AI failures by saying “the model was not good enough” is comforting but misdirected. Hallucination is a persistent risk that, as theoretical research and model providers’ own analyses indicate, must be managed through system design. The fact that products marketed as eliminating hallucination through RAG still hallucinated in 17–33% of independent tests demonstrates why this management is necessary.
Management is layered and does not require replacing the model: grounding, permission to say “I don’t know,” verification, narrow scope, and human approval for critical decisions. The first question when building an AI system should not be “Which model?” but “When this model is wrong, who will detect it, where, and how?” Our AI Systems & Agents approach begins with that question.
Frequently asked questions
What is AI hallucination?
It is an output from a language model that sounds fluent and confident but is actually false or fabricated. There are two main types: factuality errors, which contradict the external world, and faithfulness errors, which contradict the model’s own source. The model does not verify truth; it generates statistically likely text. A wrong answer can therefore sound polished and convincing.
Can AI hallucination be eliminated completely?
Based on current knowledge, no. Xu, Jain, and Kankanhalli (2024) formally argue that hallucination cannot be eliminated. Their proof uses a formal definition and does not directly cover every practical system, but the practical conclusion is that no promise of zero hallucinations should be trusted. Systems should be designed on the assumption that errors will occur.
Does RAG or grounding solve hallucination?
It reduces hallucination, but does not eliminate it. In a 2024 preregistered study by Stanford and Yale researchers, legal AI tools using RAG and marketed with “hallucination-free” claims still hallucinated in 17–33% of queries, compared with 43% for GPT-4. Grounding is necessary, but verification, abstention, and human control are still required.
What are the steps for managing hallucination?
Use layered defenses: ground the answer in source documents, allow the model to abstain when uncertain, verify claims against the source, keep the scope narrow and tested, and require human approval for high-risk outputs. None of these layers requires changing the model; all are system-design choices.
Can AI be trusted in high-risk work?
Risk management reduces risk, but does not eliminate it. Human control points are therefore essential in law, healthcare, finance, and similar fields. In the legal-AI evaluation, some errors involved subtle mischaracterizations of real cases that would be difficult for a non-expert to detect. AI does not remove verification; it prepares the work that a person must verify.
Sources
Academic: Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli, “Hallucination is Inevitable: An Innate Limitation of Large Language Models” (arXiv:2401.11817, January 2024). Varun Magesh et al., “Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools” (arXiv:2405.20362, May 2024; Journal of Empirical Legal Studies, 2025), Stanford RegLab and Yale.
Institutional: OpenAI, “Why Language Models Hallucinate” (September 2025), on evaluation regimes rewarding confident guessing.
Last reviewed: July 2026.