AI hallucinations in code: move the guessing to where it is useful

Hallucination is the industry's word for what a model does with a gap: it fills it, confidently, with the most plausible thing it can construct. In generated code this is a defect with a name and a support ticket. But the standard responses, better prompts, sterner instructions, waiting for a model that does not do it, all miss what is actually happening. The model is not malfunctioning. It is completing an under-specified request, which is the one thing generative models do by nature. You cannot remove the gap-filling from the model. What you can do is choose where the filling happens: silently inside merged code, or visibly inside a plan a human reads. That choice, not the model, decides whether the guessing hurts you.

What a hallucination in code actually is

Strip the mystique and almost every code hallucination is the same event: the request left something unspecified, and the model resolved the ambiguity without telling anyone. The requirement did not say what happens when the cart expires mid-checkout, so the model picked something. It did not say whether the endpoint exists already, so the model assumed it did, or invented one. It did not name the team's retry convention, so the model supplied a plausible one of its own. Each of those is a decision your team never made, embedded in code your team now owns.

A large share of those failures are not the model coding badly. They are the model deciding silently. The code faithfully implements a guess nobody saw, nobody confirmed, and nobody remembers making, because nobody made it.

Why suppression fails

The instinctive fixes all try to make the guessing stop, and none of them survive contact with the mechanism. "Do not assume anything" in a prompt does not remove the ambiguity from the request; the gap is still there, and something still has to fill it for code to exist at all. A more capable model does not guess less; it guesses more convincingly, which makes the silent decision harder to spot in review, not easier. And demanding fully specified requirements before anyone prompts is the waterfall fantasy: no team has ever written a requirement with no gaps, and no team ever will.

Relocation: the same instinct, pointed at a draft

Here is the reframe that changes the economics. At draft time, the model's compulsion to fill gaps is not a liability. It is the most useful thing about it. Ask it to expand a two-sentence feature request into a full plan and it will propose the edge cases nobody wrote down, name the failure modes the requirement skipped, and surface the questions the team would otherwise have discovered in production. Those are guesses too. The difference is that a guess written as a sentence in a plan is a gift: a reviewer confirms or corrects it in seconds. The identical guess compiled into merged code is a landmine: it costs a production incident to even find.

So the discipline is not "stop the model guessing". It is a relocation with four moving parts:

1. Make the model guess on paper first. Every feature becomes a structured draft plan before it becomes code. The gap-filling happens there, at full aggression, where thoroughness is the point.
2. Force the guesses to identify themselves. The draft must flag what the requirement never specified and label forced guesses as assumptions ("Assuming X") rather than stating them as fact. An unlabeled guess in a plan is still better than one in code, but a labeled one is reviewable at a glance.
3. Attack the draft before you approve it. Interrogate the plan for the gaps it did not fill and the assumptions it did not label. Questions are cheapest before anything is built on the answers.
4. Only generate code from the approved plan. Once the humans have confirmed or corrected every visible guess, code generation becomes constrained execution instead of open-ended invention. The gaps are closed before the code exists, so the model has nothing left to fill silently.

How REEZN implements this

REEZN is a governed spec-driven development platform, and this relocation is built into its generation rules, not left to prompt discipline.

• The analysis stage is where the model guesses out loud: a two-sentence ask becomes a structured plan proposing the edge cases, risks, and failure modes the requirement skipped, for the team to confirm or strike.
• Thin input is handled honestly by rule: the generation contract requires the AI to flag every vague or missing input in a dedicated Requirement Ambiguities section and to mark forced guesses explicitly as assumptions, instead of confidently inventing filler.
• A "poke holes" pass attacks the draft on demand: pointed questions about undefined failure modes, unverifiable criteria, and scope gaps, each posted as a resolvable comment before anything advances.
• Two human approval gates sit between the guessing and the code: the requirement owner confirms the plan is the right thing, a technical reviewer confirms it is the right way. An optional AI red team pass critiques the draft adversarially before they do.
• Implementation only ever runs from the approved blueprint, under conservative constraints: the smallest change that satisfies the plan, existing structures over new ones. By the time code is generated, the gaps have been filled by people, on the record.