Generative artificial intelligence has become a visible part of commercial illustration, visual communication, and design education, but current discussions often move too quickly from image generation to broad claims about creativity. This paper develops a process-oriented account of AI-assisted commercial illustration by combining a transparent focused review of thirty core references with aggregate analysis of a 3000-prompt illustration-related sample derived from a public Stable Diffusion prompt dataset. The methods specify the sample construction procedure, keyword filter, deduplication rule, preprocessing strategy, five prompt-control dimensions, and co-occurrence modelling procedure. The analysis identifies five prompt-control dimensions: illustration core, commercial visual framing, style-quality intensification, platform aesthetics, and human-reference styling. Instead of treating prompts as neutral inputs, the paper argues that prompt language functions as a compressed design interface that reallocates creative control across brief interpretation, prompt translation, generative exploration, curation, post-editing, and ethical review. The contribution is a methodological and conceptual model for studying generative AI in commercial illustration without reducing design practice to either technological automation or subjective artistic intuition.
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