The Mechanism
A large language model generates text by predicting the most statistically probable next token given everything that came before it. Token by token, the model selects from a probability distribution learned during training. There is no lookup step. There is no verified fact store being consulted. There is no internal flag that fires when the model reaches the edge of what it actually knows.
By design, not accident.
The model learned from text produced by humans — and authoritative human writing is confident. Scientific papers assert. Legal documents declare. Government guidance directs. The model learned that high-probability, well-formed prose sounds like it knows what it's talking about, because in its training data, that's exactly what well-formed prose did. So when it generates text, it generates authoritative-sounding text, because those are the high-probability tokens. The confidence and the content come from the same prediction process.
A model asked about, say, the specific FISMA continuous monitoring requirements in OMB Circular A-130 will produce a fluent, structured, confidently-toned answer. The section numbers will be plausible. The language will sound like policy. Some of it will be accurate. Some of it will be fabricated. The model cannot tell you which is which, because it has no access to the distinction. It is not retrieving A-130 from a verified source and summarizing it. It is predicting what a summary of A-130 would probably look like, based on patterns in training data that may be incomplete, outdated, or simply wrong.
"Hallucination" is the technical term for this: outputs that are statistically coherent but factually unmoored. The term is slightly unfortunate because it implies the model is experiencing something. It isn't. It's doing arithmetic that occasionally produces confident nonsense.
Okta Concept Mapping
The closest IDAM analog is a SAML IdP issuing assertions from stale cached data rather than doing a live directory lookup — well-formed, properly signed, and wrong because the underlying state changed and the system didn't check. The analogy holds up to a point: both produce confident, structurally correct outputs that don't reflect current ground truth. Where it breaks is the fix. In IDAM, you tighten the TTL and force re-authentication, because the directory has the right answer — the system just needs to be made to consult it. With LLM hallucination, there is no directory. Forcing the model to "check" requires bringing ground truth in from outside, architecturally, not configuring the session lifetime. The IDAM instinct says tighten the loop. The AI reality says build a different loop entirely.
What Grounding Does
The mitigation family goes by several names — retrieval-augmented generation (RAG), citations, tool-use, grounding — and they all share a common structure: give the model access to verified external context at inference time, so it's predicting against real information rather than pure training distribution. RAG, specifically, retrieves relevant documents and injects them into the model's context window before generation. Citations ask the model to reference specific sources so outputs can be checked. Tool-use lets the model query live systems — databases, APIs, authoritative registries — and incorporate the results.
None of these eliminate hallucination. They constrain it. A model with access to the actual text of OMB Circular A-130 will produce more accurate summaries than one working from memory. But it can still misread the document, selectively emphasize, or confabulate when the retrieved context doesn't fully cover the question. Grounding reduces the surface area of the problem. It does not close it.
When your buyer asks whether their AI deployment is "grounded," the answer is almost certainly yes, and it still isn't sufficient on its own.
The Conversation You're Walking Into
Federal CIOs and CAIOs are increasingly asking AI vendors about hallucination rates. Some vendors will quote benchmark numbers — accuracy on standardized evaluation sets, often in the 85–95% range depending on task type. These numbers are real and they are not what the buyer should be optimizing for.
Aggregate accuracy rates miss the point. What matters is what happens when the model is wrong on the specific output that drives a consequential decision. A procurement recommendation. A policy interpretation. A security assessment. An 88% accuracy rate means roughly one in eight outputs contains an error. In a workflow where a human reviews every output before acting on it, that's manageable. In a workflow where the AI output goes directly into a decision, that's a liability.
So the calibrated trust model comes down to this: never design a workflow where a consequential decision depends on an AI output with no verification path. A highly reliable model is still a model that cannot tell you when it's in the 12%. It will sound exactly the same either way.
This framing lands with public sector buyers because they already understand it from a different domain. Every federal system that processes sensitive data has audit logging, because you need to know what happened when something goes wrong. The AI equivalent is a human review gate, a citation requirement, a tool-use step that grounds the output in a verifiable source. The architecture of accountability, applied to a new kind of system.
When a CAIO asks you how your solution handles hallucination, a benchmark number won't build trust. A description of where the verification path lives in the workflow will.