Under the objective Iterate prompts to improve output quality, systematic prompt optimization relies on disciplined, hypothesis-driven iteration using controlled variables.
When a prompt yields a partially acceptable result, modifying a single isolated element at a time-such as refining role framing, adjusting format constraints, or adding specific negative constraints-allows the user to establish clear causality between the prompt modification and the resulting output change (Option A).
Documenting the effect of each adjustment ensures reproducible, predictable improvements. Conversely, making multiple simultaneous revisions (Options C and D) confounds the evaluation, making it impossible to determine which specific change helped or degraded output quality. Discarding work entirely on each run (Option B) eliminates incremental learning and increases iteration time.