dm-loopify

Rubric-driven iterative optimization requires defining explicit success criteria, weighted quality rubrics with observable anchors, and baseline scoring before running targeted critique and improvement loops on any codebase or subsystem.

Iterative refinement stop conditions are enforced by setting a target score threshold, detecting a progress plateau after consecutive non-improving rounds, or applying a maximum round cap to prevent endless and unproductive optimization loops.

Weighted quality rubrics apply to any target type, including single artifacts like design documents, by scoring the current baseline and iterating through targeted improvements until the artifact meets the defined threshold.

Unstructured quality improvement causes wasted iterations and local maxima because it lacks clear success criteria, measurable progress tracking, and defined optimization contracts, making ad-hoc efforts unmeasurable and inefficient.

Rubric optimization works for open-ended goals with no existing baseline by establishing an initial baseline score through weighted quality criteria, then applying targeted critique rounds until progress plateaus or the threshold is met.

Iterative optimization workflows should be avoided for straightforward tasks requiring only a single pass, as the overhead of defining weighted rubrics, baseline scoring, and stop conditions adds unnecessary complexity to simple improvements.