Choose tools by job
Break the workflow into research, ideation, image generation, image editing, vector or layout work, upscaling, background removal, copy drafting, mockups, and quality control. One tool rarely leads every category. The goal is a dependable chain with clear inputs and outputs.
Use deterministic tools for exact text, dimensions, masks, and export settings. Use generative tools where exploration and variation are valuable.
Evaluate with your own test set
Test tools on representative tasks: fur edges, dark-on-dark subjects, repeated patterns, small typography, transparent backgrounds, and identity preservation. Score fidelity, controllability, editability, speed, cost, commercial terms, and failure modes.
A striking demo is not evidence that a tool is reliable for your product mix. Repeat the same test after major model or policy changes.
Use at least three real tasks: a normal case, an edge case, and a recurring failure case. Measure cost per accepted result, including failed generations, manual editing, and rework—not only the price of one generation.
Build the smallest useful stack
Start with one research system, one generation route, one editor, and one production checklist. Add a specialized tool only when a recurring bottleneck justifies the handoff. Each extra step introduces format, color, version, and privacy risks.
Prefer tools that preserve high-resolution originals and common editable formats. Confirm current commercial-use and data-handling terms yourself.
Define each handoff's accepted format, dimensions, transparency, text status, version name, and rejection conditions. A new tool that removes no proven bottleneck usually adds system complexity instead of capability.
Record reproducibility without exposing secrets
For each approved asset, note the tool, model or version when available, date, input asset IDs, public Skill used, important settings, edits, and reviewer. This supports troubleshooting and consistent variants.
Do not place credentials, private prompts, hidden reference assets, or customer-sensitive data in public metadata or product pages.