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When a model’s output can influence a user or trigger software behavior, have it return a specific value or choice that ordinary code can validate before anything acts on it. Check the actual output or requested action, define what happens when the check fails, and keep exact values in fixed templates when the correct values are already known. These controls limit the damage a model error can cause. They do not prove the model read the world correctly.
Why the output format matters more than the prompt’s safety language
A prompt that says “be careful,” “only do safe things,” or “never delete files” gives you a hope, not a control. The model may follow that instruction most of the time and still produce a wrong answer, an invented identifier, or an action outside what you intended. The more reliable pattern is to shape the output so that software can test it mechanically.
The question to ask before writing the prompt is: what can the software verify before this output is used, and what happens if the check fails? If you cannot answer the first half, the model should not be the thing deciding that step.
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The sound.fan article “Ask the model for something your code can check,” dated September 16, 2026, describes four software projects that follow this approach. These are reported project examples. The descriptions below report what the write-up and the linked project materials say; they do not reflect independent testing of the code.
Gilbeot: turn a direction judgment into a coordinate comparison
Gilbeot is described in a Kaggle submission as an on-device walking assistant. In the pattern the article describes, the model does not answer “turn left or right” directly. It supplies the horizontal coordinates of an arrow’s tip and tail. Ordinary code compares those two numbers, derives the direction, and treats near-equal values as uncertain rather than guessing.
This makes the final direction decision deterministic once the coordinates are given. It does not prove the model found the correct arrow in the image. A wrong arrow with clean coordinates will pass the comparison without complaint.
Sentinel: validate a structured security review
Sentinel is a security scanner that uses a model to review code. According to the sound.fan write-up, the host program checks three things about the model’s output:
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- Each finding ID belongs to the batch currently being reviewed.
- Each proposed probe fits the tool’s allowed input format.
The model chooses among predefined probe options, while the host program builds the actual payload. Output that fails these checks can be retried or left in a state that requires human review. The write-up does not describe a finding’s accuracy, so validation here confirms structure and provenance, not that the vulnerability is real.
AirBridge: authorize the action, not an assumed intention
AirBridge is described as a local tool catalog. Each tool has action rules, argument limits, and confirmation requirements. Three behaviors follow from that design:
- A tool that is not in the catalog is refused, regardless of how the model phrased the request.
- Arguments are checked against their limits. A volume argument, for example, must fall within its defined range.
- Confirmation is tied to the specific tool and its specific arguments. Approving one call does not approve a different call with altered values.
The point of this pattern is that the system decides what is permitted, not the model’s stated purpose. Whether the chosen tool is the right one for the user’s goal remains a separate question the catalog cannot answer.
Rank #3
Project Rosie: template what is already known
Project Rosie’s public repository describes a veterinary-oncology AI pipeline. The sound.fan write-up says that a synthesis specification originally written by a model was replaced with a template, because the fixed manufacturing details were already known and had to remain exact. Asking a model to reproduce those details invites small, silent drift; a template cannot drift.
The write-up and repository do not establish the clinical or biomedical outcomes of this pipeline, and this article does not evaluate them. The lesson that transfers is narrower: when a value is known in advance, generating it from a template is safer than regenerating it.
What each pattern can and cannot check
The four projects are distinct implementation patterns rather than competing products, so the useful comparison is what each one checks, what it leaves uncertain, and what the system does on failure.
| Pattern | What code can check | What remains uncertain | Behavior on failure |
|---|---|---|---|
| Gilbeot (coordinate comparison) | Relation between supplied tip and tail coordinates; near-equal values flagged | Whether the model located the correct arrow | Near-equal values are treated as uncertain |
| Sentinel (structured review) | Cited lines appear in the input; finding IDs belong to the active batch; probes fit the allowed format | Whether a flagged finding is a real vulnerability | Retry, or hold for human review |
| AirBridge (tool catalog) | Tool is catalogued; arguments fall within limits; confirmation matches the exact call | Whether the permitted tool is the right one for the user’s goal | Refuse the action |
| Project Rosie (template) | Fixed values are inserted exactly as known | Clinical or biomedical outcomes (not evaluated here) | Not applicable; the template is deterministic |
Designing the checks
The examples share a few reusable checks. Choose the ones that match your output:
- Numeric relations: compare two returned numbers rather than accepting a named direction, and define a tolerance band for ambiguous cases.
- Membership: confirm identifiers belong to a current set, and confirm cited evidence appears in the input you supplied.
- Schema and range: enforce the tool’s input format and argument limits before execution.
- Allowlisted actions: authorize only catalogued tools, and bind any confirmation to the exact arguments.
- Fixed values: insert known constants from a template instead of asking the model to write them.
Defining the failure path
A check without a defined failure path only produces an error log. Decide the response in advance:
- Reject output that fails a structural check, such as a finding ID outside the current batch.
- Retry once or a bounded number of times when the failure looks like formatting noise, as in Sentinel’s probe selection.
- Route to human review when the output is valid in form but ambiguous, such as Gilbeot’s near-equal coordinates.
- Refuse the action when the requested tool or argument is outside the catalog or range, as in AirBridge.
- Fall back to a template where the correct values are already known.
Where validation stops
Every check above verifies the shape, provenance, or permission of an output. None of them verifies the model’s underlying perception or reasoning. A coordinate can be well formed and still point at the wrong object. A cited line can exist in the input and still be misread. An authorized tool call can be the wrong call for the user. Treat validation as a gate that catches a class of errors, and keep the question of whether the model understood the situation as a separate concern that needs testing and, where stakes are high, a human decision.
Best Value
The sound.fan write-up does not provide named statistics or expert quotations supporting this principle, so the case rests on the design logic and the four implementation examples rather than on measured error rates.
Applying the principle to your own system
Start by listing every place where model output reaches a user, a database, a tool call, or a fixed deliverable. For each one, write down the check that software can run and the failure response you will use. Where the model is choosing among known options, constrain it to those options. Where the values are already known, remove the model from producing them altogether.
This approach does not make a model trustworthy. It makes the model’s mistakes easier to see, easier to refuse, and harder to act on by accident.
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