Image Generation Tools and Prompts

The concept

AI image tools — Midjourney, DALL-E, Stable Diffusion-based tools, and the image generators built into most chat assistants now — work by the same underlying logic as the text models earlier in this course: they've learned statistical patterns from enormous amounts of training data (in this case, image-caption pairs) and generate new pixels that match the pattern described by your prompt. Just as an LLM predicts the most plausible next token, an image model predicts the most plausible arrangement of pixels given a text description, refining a noisy starting image step by step until it resembles what you asked for. This is useful background even if you never touch the underlying mechanics directly, because it explains why image prompting rewards the same instinct as text prompting: specificity beats vagueness, every time.

A strong image prompt has four ingredients, and leaving any one out forces the model to guess. Subject — what's actually in the image, described concretely ("a red fox," not "an animal"). Style — the visual treatment ("minimalist line art," "photorealistic," "90s anime," "watercolor"), since the same subject renders completely differently depending on style. Composition — framing and arrangement ("close-up," "centered," "wide shot with empty space on the left for text"). Lighting — often the single biggest lever on mood and quality ("soft natural light," "dramatic side lighting," "flat even lighting for a product shot"). A prompt missing style and lighting entirely still produces an image — the model fills the gaps with whatever is statistically most common for the subject — but it won't be predictable, and predictability is what you need for real work.

Beyond the four core ingredients, most tools let you set a few technical parameters worth knowing even if you rarely touch them: aspect ratio (square for social avatars, wide for banners, tall for mobile-first posts), a reference image to guide style or composition if the tool supports "image + text" prompts, and a model or quality setting where a faster/cheaper option trades fidelity for speed. None of these replace a clear text prompt — they shape it — so treat the written prompt as the foundation and the parameters as fine-tuning on top of it, not a substitute for specificity.

The other core skill is iteration. Image generation is rarely a one-shot process: the right workflow is to generate a batch of 2–4 variations from one prompt, pick the one closest to what you need, and then adjust the prompt — not regenerate from scratch — to nudge it further. Common refinements include adding a negative instruction ("no text," "no extra fingers"), tightening the style description, or adding a reference detail you noticed was missing in the batch. Treat the first prompt as a starting point for a short back-and-forth, not a final attempt graded pass/fail. Keep a short log of what you changed between rounds — it's the fastest way to learn which words actually move the output for the tool you're using, since the same phrase can behave differently across models.

Worked example

A learner needs a logo concept and starts with the prompt "a logo." The result is generic and unusable — a vague circular emblem with no clear subject, style, or composition, because the model had almost nothing to anchor on and defaulted to the most statistically average "logo-shaped" image in its training data.

They revise to: "a minimalist line-art logo of a fox, single color, white background, vector style, centered composition, no gradients." The jump in usability is immediate — the new prompt specifies subject (fox), style (minimalist line-art, vector, single color), background (white), and composition (centered), leaving almost nothing to the model's default guesswork. The resulting image is a clean, usable starting point for a real logo, where the first attempt was not.

The learner then iterates once more: of the four variations generated, one has an odd double-line artifact on the fox's tail. Rather than starting over, they add "clean single-stroke lines, no overlapping linework" to the same prompt and regenerate, producing a cleaner result on the second pass. For a final refinement, they set a square aspect ratio since the logo needs to work as a small social avatar, and generate one more batch — confirming the composition still reads clearly at a small size, which a wide or tall framing would not have.

Exercise

Pick a real visual need from your own work or a project — a social media graphic, a simple icon, a header image for a document. Write an image prompt that specifies all four ingredients: subject, style, composition, and lighting. Generate a small batch of variations (if your tool supports it) or a few separate generations.

Select the one closest to what you need, and write one specific refinement to the prompt (a negative instruction, a style tweak, or a missing detail) to improve it further. Set an appropriate aspect ratio for where the image will actually be used (square, wide, or tall) and regenerate once with that constraint included.

Note, in one sentence, what changed between your first vague attempt (if you tried one) and the fully-specified version — this is the same lesson as prompt specificity in Module 2, applied to a different modality.

Recap

• Image prompts need the same specificity as text prompts: subject, style, composition, and lighting are the four ingredients that separate a usable result from a generic guess.
• Expect to iterate across a few generations rather than get the right result in one shot — pick the closest variation and refine the prompt from there, and use technical parameters like aspect ratio to fit the output to where it will actually be used.
• A missing ingredient doesn't produce an error, it produces an unpredictable default — vagueness in image prompting is invisible until you compare it to a specified version side by side.

Resources

• OpenAI: "DALL·E prompting guide" — https://platform.openai.com/docs/guides/images
• Midjourney documentation — https://docs.midjourney.com/
• Stability AI: Stable Diffusion prompt guide — https://stability.ai/learning-hub