A single overall score can tell you a draft changed without showing what changed. With the Laravel AI SDK and Jev, you can ask three separate questions about one draft in a single request: whether it meets its brief, what kind of text it is, and how clear its instructions are. The results are distinct signals—not a combined verdict—and they cannot condition on one another.
Why ask three separate questions?
Imagine an editor sees a draft rated “7 out of 10.” That illustrative score does not reveal whether the draft missed its brief, was put in the wrong category, or explained its steps poorly. Separate results make those dimensions visible so a person or application can respond to the specific issue.
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In his October 6, 2026 Laravel News tutorial, Harris Raftopoulos describes this as a design choice: “Three separate answers point straight at the part that changed.” It is a rationale for structuring the assessment, not evidence that the approach has been empirically proven more accurate.
What the three checks assess
| Result type | Question | Example use |
|---|---|---|
| Boolean | Does the draft fulfill its brief? | Flag a draft that does not meet the requested purpose or requirements. |
| Choice | Is the text a tutorial, announcement, opinion piece, or other? | Classify the draft while allowing for formats outside the listed categories. |
| Score | How clear and verifiable are the instructional steps? | Identify whether a reader can follow and check the instructions. |
Each output answers a different question. The Boolean is a yes-or-no check, the choice selects a category, and the score places clarity against defined levels. None should be treated as proof that the assessment is correct.
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Run the checks together, then handle dependencies in PHP
Jev answers the three questions at the same time. Each question is independent: it does not receive the answers to the other questions as context. That makes the request suitable when the checks can stand on their own, but not when one answer must alter another check’s rule.
For example, if a tutorial should be scored by a different clarity rubric than an announcement, the score question cannot use the format answer from the same response. Instead, inspect the returned format in PHP and apply the appropriate logic after the response, or make a subsequent request with the needed context.
Keep the question set named and versioned
Raftopoulos’s pattern is to put the questions in a named, versioned service that both the application and its tests use. This keeps the tested question set aligned with the one the application runs. When the rules change, create a new version so callers and tests can identify which set they depend on.
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Interpret choice and score results carefully
Choice: selected option, likelihoods, and confidence
A choice result includes the selected option and likelihoods, as well as a confidence number describing how clear the selection is. Treat these as model outputs to inform a workflow, not as confirmation that the classification is true.
Score: levels and normalized values
A score can fall between defined levels. The tutorial uses level() to retrieve the most likely level and normalized() to express the score on a 0–1 scale. These methods help interpret the result in an application; they do not turn it into a verified measure of writing quality.
Source and walkthrough
The implementation pattern and result interpretation are covered in Harris Raftopoulos’s Laravel News tutorial, which links to a nine-minute video and the harris21/decide-with-jev GitHub repository.
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