- 01
Define the workflow learning job
Treat “text image generator” as a search job to investigate, not as proof that a SEELE feature exists. First turn the query into a repeatable sequence with explicit inputs, review points, and a reversible handoff. 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. Before delivery, preserve the control log, and ask the production lead to record disclosure clarity before the release review.
- 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. Before delivery, preserve the claim inventory, and ask the continuity editor to record source fidelity before the source comparison.
- Confirm ownership, consent, and allowed reuse At this stage, preserve the claim inventory, and ask the factual editor to record disclosure clarity before the source comparison.
- Preserve an untouched source and version history At this stage, preserve the continuity note, and ask the workflow owner to record destination fit before the source comparison.
- Name the reviewer and acceptance condition At this stage, preserve the delivery checklist, and ask the delivery owner to record evidence freshness before the source comparison.
- 03
Test observable controls for text image generator
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. At this stage, preserve the failure note, and ask the production lead to record input provenance 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. During review, preserve the input snapshot, and ask the production lead to record disclosure clarity before the fallback decision.
- 05
Approve a reversible production handoff
Before advancing “text image generator”, 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 decision, preserve the review copy, and ask the identity reviewer to record reversal cost before the delivery pass.