AI Skill Report Card
Engineering Image Prompts
YAML--- name: engineering-image-prompts description: Crafts detailed, technically precise prompts for AI image generation platforms (Midjourney, DALL-E 3, Stable Diffusion, Flux) by translating abstract visual concepts and photography terminology into structured prompt language. Use when generating AI image prompts, refining existing prompts for better output quality, translating mood boards or brand references into prompt text, or building consistent visual identity across multiple generated images. --- # Engineering Image Prompts
Quick Start15 / 15
Given a request like "a moody portrait for our app's login screen," produce:
Subject: Close-up portrait of a young professional woman, contemplative expression, looking slightly off-camera
Environment: Minimalist dark studio background, subtle gradient from charcoal to black
Lighting: Single soft key light from camera-left at 45°, low-key Rembrandt lighting, soft shadow falloff on right side of face
Style: Editorial photography, reminiscent of Annie Leibovitz portraiture, muted color grade with slight teal-orange contrast
Technical: Shot on 85mm lens, f/1.8 aperture, shallow depth of field with creamy bokeh, ISO 200, sharp focus on eyes
Composition: Rule of thirds, subject positioned right third, negative space left for UI text overlay
Aspect ratio: 3:4 vertical
Negative prompt: harsh shadows, overexposed highlights, cluttered background, extra fingers, distorted features, blurry eyes, low resolution, watermark
Adjust syntax per platform (Midjourney uses --ar 3:4 --style raw, DALL-E favors natural language sentences, Stable Diffusion/Flux use comma-separated tags with explicit negative prompt fields).
Recommendation▾
Add an example showing a failed/bad prompt output versus corrected version to illustrate pitfalls concretely
Workflow14 / 15
Progress:
- Clarify the visual goal (subject, mood, use case — hero image, placeholder, marketing asset)
- Identify target platform(s) and their prompt syntax conventions
- Draft structured prompt: Subject → Environment → Lighting → Style → Technical → Composition
- Translate any vague creative direction into precise photography terminology
- Add negative prompt to exclude common artifacts and unwanted elements
- Specify aspect ratio and platform-specific flags/parameters
- Review for technical consistency (lighting direction matches shadows, lens choice matches described depth of field)
- If brand consistency is needed, extract reusable lighting/color/composition parameters for reuse across prompts
Recommendation▾
Include guidance on iterating based on model output (e.g., troubleshooting artifacts after generation)
Structured Prompt Framework
Every prompt should layer information in this order:
- Subject — who/what, pose, expression, key details
- Environment — setting, background, props, context
- Lighting — direction, quality (hard/soft), color temperature, time of day or studio setup
- Style — photographic genre, reference photographers/movements, color grade
- Technical specs — lens focal length, aperture (f-stop), depth of field, ISO/grain, focus point
- Composition — framing, rule of thirds/centered, negative space, aspect ratio
- Negative prompt — artifacts, distortions, unwanted elements to exclude
Platform-Specific Notes
- Midjourney: Append
--ar W:H,--style rawfor photorealism,--stylizefor artistic drift,--no [elements]for negatives. Prefers dense comma-separated phrases over full sentences. - DALL-E 3: Prefers natural, descriptive sentences rather than tag lists. No native negative prompt field — instead phrase exclusions positively ("clean background" instead of "no clutter").
- Stable Diffusion: Explicit separate negative prompt field. Tag-based syntax works well. Weight important terms with
(term:1.2)syntax if supported. - Flux: Similar to Midjourney but more literal — avoid overly poetic language, favor concrete technical descriptions.
Photography Terminology Translation
| Vague input | Technical translation |
|---|---|
| "blurry background" | "shallow depth of field, f/1.8 bokeh" |
| "nice lighting" | "soft golden hour side lighting, warm color temperature ~3200K" |
| "professional look" | "studio strobe lighting, three-point setup, softbox key light" |
| "moody" | "low-key lighting, high contrast, desaturated shadows" |
| "close up" | "85mm portrait lens, tight framing, subject fills 60% of frame" |
| "wide shot" | "24mm wide-angle lens, environmental context visible" |
Examples18 / 20
Example 1: Input: "Product photo of a coffee mug, clean and minimal, for an e-commerce site" Output:
Subject: Ceramic coffee mug, matte white finish, centered, no handle-side rotation distortion
Environment: Seamless white backdrop, subtle floor reflection
Lighting: Softbox lighting from both sides at 45°, even diffusion, minimal shadow, high-key setup
Style: Commercial product photography, clean e-commerce aesthetic
Technical: Shot on 100mm macro lens, f/8 for full product sharpness, studio strobes, ISO 100
Composition: Centered subject, ample negative space, square crop
Aspect ratio: 1:1
Negative prompt: harsh shadows, reflections of camera equipment, dust, fingerprints, uneven lighting, background clutter, color cast
Example 2: Input: "Something for our brand's hero banner — outdoorsy, adventurous, golden light" Output:
Subject: Solo hiker viewed from behind, mid-stride on a ridge trail, backpack visible
Environment: Mountain ridge line, distant peaks with atmospheric haze
Lighting: Golden hour backlighting, sun low on horizon, warm rim light on subject's silhouette, lens flare
Style: Adventure/travel editorial photography, National Geographic aesthetic, warm color grade with lifted shadows
Technical: Shot on 35mm lens, f/4 for environmental sharpness, ISO 100, natural light only
Composition: Rule of thirds, subject lower-left third, expansive sky for text overlay, wide aspect
Aspect ratio: 21:9
Negative prompt: overexposed sky, harsh midday shadows, cluttered foreground, motion blur, low contrast haze, oversaturated colors
Recommendation▾
Consider a third example for Stable Diffusion/Flux-specific tag-based syntax to show platform translation in action
Best Practices
- Always specify aspect ratio explicitly — never leave framing to default.
- Match lighting direction to shadow description; inconsistencies confuse the model and degrade output quality.
- Use real photographer/style references sparingly and accurately (e.g., "Annie Leibovitz portraiture," "Peter Lindbergh black-and-white") — they carry strong stylistic weight.
- For brand consistency, lock in a reusable "style block" (lighting setup + color grade + lens choice) and reuse verbatim across all prompts in a campaign.
- Always include a negative prompt on platforms that support it, even a short default one (extra limbs, watermark, blurry, low quality, distorted).
- When precision matters, favor technical photography terms over adjectives — models trained on captioned photo datasets respond strongly to f-stop, focal length, and lighting-setup vocabulary.
Common Pitfalls
- Don't use vague adjectives ("beautiful," "amazing," "high quality") as substitutes for concrete description — they add tokens without adding signal.
- Don't request physically implausible combinations (e.g., "hard shadows" with "overcast diffused lighting") — this produces inconsistent results.
- Don't skip aspect ratio — default ratios rarely match the intended use case (UI placeholder, banner, portrait).
- Don't reuse Midjourney tag-syntax prompts unmodified for DALL-E — rewrite as natural sentences for best results.
- Don't forget negative prompts on Stable Diffusion/Flux — omitting them is the most common cause of artifact-heavy output.