- 01
Define the generation workflow job
Treat “text to image ai” as a search job to investigate, not as proof that a SEELE feature exists. First write a shot or asset contract, test one controlled variation, and plan the human finishing work. Write the intended audience, source owner, desired change, protected details, reviewer, and delivery condition before selecting any interface or model. That brief keeps the evaluation specific and makes an unsupported assumption visible early. At the next gate, preserve the input snapshot, and ask the identity reviewer to record source fidelity before the scope confirmation.
- 02
Prepare inputs for image generation
For this topic, assemble a visual brief, authorized references, composition goals, style constraints, exclusions, and output requirements. Record where each source came from, who may use it, and what must remain unchanged. Use a small representative asset for the first pass, keep the original untouched, and define a fallback route so experimentation cannot silently become the production master. At the next gate, preserve the continuity note, and ask the identity reviewer to record evidence freshness before the scope confirmation.
- Confirm ownership, consent, and allowed reuse Before revision, preserve the reference set, and ask the identity reviewer to record input provenance before the controlled revision.
- Preserve an untouched source and version history Before revision, preserve the delivery checklist, and ask the policy reviewer to record identity consent before the controlled revision.
- Name the reviewer and acceptance condition Before revision, preserve the continuity note, and ask the creative lead to record claim scope before the controlled revision.
- 03
Test observable controls for text to image ai
A bounded evaluation should inspect subject fidelity, composition, typography, material detail, variation strategy, and revision consistency. Change one meaningful variable at a time and record the date, workspace, account context, input, setting, result, and failure. Topic selection can prioritize the question, but it does not establish availability, quality, speed, licensing, or a supported SEELE workflow. Before revision, preserve the evidence table, and ask the claims reviewer to record control availability before the source comparison.
- 04
Review evidence, safety, and policy boundaries
A search query does not prove access to a model, commercial rights, exact dimensions, or consistent output quality. Use only authorized media, separate observed behavior from marketing language, and check current first-party documentation for any product-specific claim. For third-party products, competitors, plans, models, and platform rules, attach a verification date and primary source; an absent statement is an evidence gap rather than proof of a limitation. Before delivery, preserve the brief version, and ask the production lead to record claim scope before the release review.
- 05
Approve a reversible production handoff
Before advancing “text to image ai”, compare candidates with the brief, inspect fine detail and text, and record which instruction caused each useful change. Document remaining manual work, unresolved evidence, destination requirements, and the person accepting the result. The handoff should preserve sources and test notes, allow correction, and avoid promises about output quality, turnaround, business performance, publishing, or access that the evidence does not support. For this checkpoint, preserve the handoff draft, and ask the accessibility reviewer to record evidence freshness before the evidence refresh.