Use prompt moves that transfer

First, orient the model with the real audience and decision. Second, ground it in supplied sources. Third, constrain scope, format, and forbidden actions. Fourth, inspect by asking for assumptions, unsupported claims, or tests. Applied to this topic, those moves support diagnose a confusing dashboard screenshot using image evidence plus written context, not vague content generation.

Inspect this redacted checkout dashboard screenshot. Goal: explain why the conversion total appears inconsistent. First inventory only visible labels and values with locations. Separate observation from inference. Then propose three hypotheses and the next screenshot or log needed to test each. Do not identify people, infer hidden fields, or invent unreadable text.

The likely useful output is: An observation table grounded in screen regions, clearly labeled hypotheses, and targeted follow-up evidence instead of a confident visual guess. Follow with a critic pass, not a request to “improve it”:

Audit the draft against the original contract. Return a table:
criterion | pass/fail | exact evidence | smallest correction.
Do not introduce new facts. List unresolved questions separately.

This second prompt changes the mode from creation to inspection. For alternatives, request deliberately different options and specify the axis of difference. For revision, name one defect and freeze everything else. For extraction, require a schema and define unknown/null behavior. For decisions, ask for criteria, evidence, assumptions, and sensitivity—not hidden private reasoning.

A useful response would look like this: An observation table grounded in screen regions, clearly labeled hypotheses, and targeted follow-up evidence instead of a confident visual guess. That description is intentionally observable. “Looks good” is not acceptance. The operator must zoom and compare source pixels, transcribe critical values manually, reproduce in the product, inspect logs, and reject claims unsupported by visible or supplied evidence. Keep the source material beside the draft so review means comparison, not memory.

Do not confuse fluent explanations with evidence. Point to regions, labels, frames, or timestamps. When the model cannot read a detail reliably, provide a crop or transcription instead of asking it to guess harder. The prompt is successful only when the resulting artifact survives an external check.

Watch for hallucinated small text; mixing observation with inference; ignoring chart scale; accidental exposure around the crop; treating an image as current truth. If the answer is too broad, shrink the deliverable. If it invents, tighten “use only” boundaries and require source labels. If formatting drifts, provide a short valid example and validate mechanically. If every option sounds alike, define meaningful axes. If revision damages good sections, quote the exact passage to preserve.

Keep prompt versions with short notes: what changed, why, and what happened. That creates transferable knowledge. Copying a “perfect prompt” without its data, risk level, and reviewer rarely does.