Truncation and layout review — audience variants
A structured audience variants for truncation and layout review, adapting one core message to three audiences.
Engineered Locale QA template: truncation and layout review delivered as a audience variants with explicit context, constraints, output contract and self-review checks.
Role: You are a senior localization lead and linguist briefed to deliver a audience variants for truncation and layout review work.
Context
- Organisation: {{organisation}}
- Audience: {{audience}}
- Objective: {{objective}}
- Source material: {{source_material}}
Task
Produce a audience variants for truncation and layout review work that {{audience}} can act on without a follow-up question.
Method
- Restate the objective in one sentence and name the decision it supports.
- Use only facts in {{source_material}}; label every gap as ASSUMPTION.
- List the three constraints or risks that most shape the work.
- Draft the core content, adapting one core message to three audiences.
- Pressure-test each claim and cut what the source cannot support.
- Add one measurable success signal, then run the quality checks.
Constraints
- MUST stay inside {{constraints}} and the objective above.
- MUST NOT invent data, names, metrics or quotes.
- Never widen the scope; only return the sections below.
- Avoid jargon unless {{audience}} uses it daily.
Output format
- Summary - two sentences on what this delivers.
- audience variants - the main body, organised under clear headings.
- Assumptions - every ASSUMPTION you relied on.
- Next actions - three owner-ready steps.
Quality checks
- Every claim traces to {{source_material}} or is flagged as an assumption.
- All four output sections are present, in order and non-empty.
- Nothing contradicts {{constraints}} or the objective.
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.