Structure Before Generation
Purpose
This page documents how input requests are translated into an explicit, inspectable structure before any generative output occurs. This layer exists to prevent ambiguity, scope expansion, and post hoc reinterpretation once generation begins.
In practice, this is the difference between asking an AI to “write something” and requiring it to populate a defined artifact with known sections, fields, and constraints before any prose is generated.
It records the framing rules, required elements, and ordering decisions that govern later layers of the system and constrain what generation is allowed to produce.
Scope
Includes request framing, definition of target artifacts, required fields, allowed formats, and ordering rules used to assemble information in a stable, reviewable form. Excludes model specific prompting tactics, content writing guidance, stylistic decisions, and any statements about effectiveness or outcomes.
Constraints
- The target artifact and its acceptance criteria are explicitly defined before any content is drafted.
- User provided facts are distinguished from inferred assumptions, with assumptions labeled and tracked.
- Structural patterns favor minimal, stable forms unless additional complexity is required by a documented dependency.
- Naming, hierarchy, and section boundaries remain consistent to preserve navigation, retrieval behavior, and metadata stability.
These constraints are treated as fixed controls rather than preferences. Relaxing them shifts ambiguity downstream, where it is more difficult to detect and correct.
Notes
Output structures are intentionally simple enough to audit quickly while remaining strict enough to prevent silent drift across edits, reuse, and deployment contexts.
If a proposed change would weaken constraints, blur boundaries, or reduce credibility, it must be identified and reviewed before being applied. Once generation occurs against a weakened structure, downstream layers cannot consistently restore lost intent or control.