A brief states the standard a model should learn in structured natural language. It is supervision by description: you articulate what good looks like, and the build synthesizes labelled samples from it before training.Documentation Index
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Definition
A brief is articulated intent — the standard expressed as words rather than examples. It suits the common starting point where you can describe the judgment you want but have no labelled dataset to hand. From a brief, the build generates the examples a model needs to train.Mechanism
A brief that produces a usable model covers six elements:| Element | What it states |
|---|---|
| Intent | What the model is for, and the decision it supports. |
| Domain | The kind of content, its context, and its audience. |
| Exemplars of good | The qualities that should score well — described, not listed. |
| Failure modes | The anti-patterns and common ways content misses. |
| Edge cases | Tricky calls you want handled a particular way. |
| Constraints / non-goals | What the model should not optimize for. |
Interpretation
- A brief trades precision for speed. It is the fastest way to a first scoring model, and a strong starting point to refine with direct samples or feedback once real content flows.
- Describe qualities, not instances: “concrete and free of hedging” generalizes to new content; a list of ten good sentences does not.
- Name the failure modes explicitly. The negative pole shapes a trait’s geometry as much as the positive one, so a brief that only describes “good” leaves the boundary underspecified.
Edge cases
- A brief and supplied samples can coexist: synthesized examples fill the gaps a partial dataset leaves.
- Because a brief resolves to samples, a model built from one produces the same score card shape as a model built from data.
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Samples
What a brief is synthesized into.
Authoring schemas
Where a trait’s minimal specification is declared.