A language model doesn't know anything. It has learned, from an enormous corpus of text, which tokens tend to follow which other tokens in which contexts. That's the complete mechanism. When you ask it a question, it doesn't retrieve a fact. It predicts the most probable sequence of tokens that would follow your question, given everything it absorbed during training.
This is why hallucination isn't a bug. It's what the architecture does when it runs out of signal.
The Mechanism
When a model encounters a topic where its training data was dense — federal acquisition regulations, NIST frameworks, common programming patterns — it's predicting against a rich distribution. The output is likely to be accurate because the patterns it learned were accurate. When it encounters a topic where training data was sparse — a specific agency's internal process, a recent policy change, a niche technical specification — it keeps predicting tokens anyway. The output is still fluent. The confidence is identical. The accuracy is not.
There's no internal flag that says "I'm uncertain here." The architecture has no mechanism to distinguish "I have strong evidence for this" from "I'm extrapolating into thin air." It just keeps generating.
That's the structural problem. The model's output confidence is a function of its training — specifically, how well it learned to produce fluent, coherent text. That's a different thing from the accuracy of any specific claim. Those two properties are correlated in aggregate: more training data on a topic generally means more accurate outputs. But they're not coupled at the level of individual sentences. A model can be highly accurate on average and confidently wrong on the specific claim that matters in your meeting.
What This Looks Like in Practice
A model is asked: "What are the FedRAMP authorization requirements for a SaaS vendor serving a DoD component?"
The model has seen a lot of text about FedRAMP. Framework documentation, agency guidance, vendor compliance write-ups, blog posts from consultants. It generates a confident, well-structured answer. Most of it is probably right. But somewhere in that answer, it might cite a requirement that was updated after its training cutoff, or conflate two different authorization paths, or produce a specific threshold that sounds plausible but isn't in the actual guidance.
The reader has no signal that this happened. The model didn't hedge. It didn't flag the uncertain parts. It produced the same fluent, authoritative prose for the accurate claims and the confabulated ones alike. That's not a failure of the model doing something wrong — it's the model doing exactly what it was trained to do, in a region of its distribution where the training signal was thin.
Grounding: What It Is and What It Isn't
Grounding is the collective term for techniques that give the model better signal at inference time, so it's predicting against context that contains the actual answer rather than relying on training memory alone.
Three approaches are worth knowing.
Retrieval-Augmented Generation (RAG) retrieves relevant documents from a corpus at inference time and injects them into the model's context window. The model generates against those documents rather than against training memory alone. Ask about FedRAMP requirements, and the system retrieves the current FedRAMP documentation before the model responds. The model is now predicting tokens against the actual source. (How retrieval works, embeddings and vector search, was covered in Lesson 3. Reference only here.)
Citation-enforced generation requires the model to attribute each claim to a source. This creates a verification path: you can check whether the cited source actually says what the model claims. Some systems enforce this structurally; others rely on prompting. The discipline of citation doesn't prevent hallucination, but it makes hallucination detectable — which is a meaningful improvement.
Tool-use lets the model call external systems, search engines, databases, live APIs, to retrieve real-time or authoritative information rather than relying on training data. A model with access to a current policy database can look up the actual requirement rather than pattern-match toward what it thinks the requirement probably is.
All three reduce hallucination risk. None eliminate it. The model is still predicting tokens. It can misread a retrieved document. It can hallucinate a citation to a real source that doesn't say what the model claims. It can call a tool, receive accurate data, and then misrepresent that data in its output. The mechanism doesn't change — the signal quality improves.
Okta Concept Mapping: Zero Trust Gets You Halfway There
Zero trust's never trust, always verify is the right instinct for model output. Treat every generation as unverified until a retrieval path, citation, or tool call confirms it. Don't trust the confidence of the prose — fluency is not accuracy.
This is where your zero trust intuition helps. Here's where it starts to mislead you.
Zero trust verification is authoritative and binary. The token is valid or it isn't. The policy allows or it denies. There's an oracle — the authorization server, the policy engine — that produces a definitive answer. You can automate on that answer because the answer is reliable.
Grounding doesn't give you that oracle. A retrieved document reduces hallucination risk; it doesn't eliminate it. There's no binary "grounded/not grounded" state — there's a probability distribution over how accurately the output tracks the retrieved context. You can't automate on that the way you automate on a policy decision. For any consequential output, a human judgment point in the workflow isn't optional. Not because the model is bad at its job, but because the job it's doing is fundamentally probabilistic, and probabilistic systems require human verification at the point where the stakes are high enough to matter.
The Conversation You're About to Have
A federal CISO tells you their agency is piloting an AI assistant to help contracting officers summarize solicitation documents and draft justification memos. They've built a RAG pipeline over their contract corpus. They want to know if this is safe to deploy.
The honest answer: it depends on what "safe" means and what happens after the model outputs something.
If a contracting officer reads the AI-generated summary, checks it against the source document, and signs off — that's a workflow with a human verification point. The model is a drafting assistant. The risk is manageable.
If the AI-generated summary goes directly into a procurement system without a human review step, that's a different risk profile. The model might be right 95% of the time. The 5% where it's wrong might include a misread clause that affects contract terms. The model will not tell you which outputs are in the 5%.
So the question worth asking isn't "does your AI hallucinate" — every AI hallucinates. It's what happens in your workflow when it does.
The agencies that will use these systems well are the ones that design the human verification point into the workflow before deployment, not the ones that add it after something goes wrong. That's a design constraint, not a limitation to apologize for. Frame it that way.
Recap:
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Hallucination: A structural consequence of next-token prediction. Models generate fluent, confident text whether they're drawing on dense training signal or confabulating in sparse territory — the architecture has no mechanism to distinguish between them, and the output confidence doesn't track the accuracy of individual claims.
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Grounding: The family of techniques — RAG, citations, tool-use — that improve model output by providing better signal at inference time. Grounding reduces hallucination risk; it does not eliminate it. The model is still predicting tokens against the retrieved context, and it can still get that wrong.
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The zero trust analogy: Useful for establishing the right posture (never trust model output without a verification path), but it breaks at a critical point. Zero trust verification is authoritative and binary. Grounding is probabilistic. For consequential decisions, a human judgment point in the workflow is not optional — it's the design requirement that grounding alone cannot satisfy.