Describing People: Features and Inclusive Prompting

The words you use to describe human subjects in AI prompts do not just control visual output — they encode assumptions about who is default and who is specific. When you write "a doctor" without further specification, most models will generate a narrow demographic profile because the training data over-represents certain types of people in certain roles. This is not just an ethical concern — it is a practical one for any brand or creator producing imagery for a real audience: if your subject descriptions do not reflect the actual diversity of your audience, your imagery will feel exclusionary to the people it does not represent. Conversely, describing subjects with specific, accurate, and respectful feature vocabulary produces richer, more distinctive imagery and creates a content library that actually reflects the complexity of human appearance. The goal of inclusive prompting is not to assign demographic labels mechanically, but to describe real human feature variation with the same specificity and care you would bring to describing a location or a material.

Skin tone is one of the most important visual descriptors for human subjects, and the most common approach — naming a racial or ethnic group — is also the least accurate and most prone to stereotyped associations. A better approach is to describe the actual visual properties of the skin tone using descriptive, specific vocabulary: "deep ebony complexion with warm undertones," "medium caramel skin tone, cool undertones," "olive-toned complexion with golden sheen," "fair porcelain skin with pink undertones," "tan with warm golden-brown cast," "deep burgundy-brown complexion with rich melanin depth." This vocabulary describes what you actually see — or want to see — in the image, rather than assigning a demographic category that the model then interprets through its training-data associations. The Monk Skin Tone Scale (MST-10) is a useful reference system for thinking systematically about the full range of human skin tones and can guide your vocabulary toward more consistent, repeatable descriptions across a production batch.

Hair texture and type vocabulary covers enormous natural human variation that is systematically under-represented in generic AI imagery, which tends to default to straight or loosely wavy hair. Describe hair type using the 1-4 classification system or descriptive equivalents: "Type 4C coily hair in a defined twist-out," "Type 3B loose corkscrews, high volume," "Type 2A gentle beach waves," "straight fine hair," "kinky natural hair in a high puff," "locs in shoulder-length freeform style," "tight coils in a cropped fade," "long box braids with gold cuffs." For hairline and styling specifics: "natural hairline, no makeup product," "protective style," "silk press blowout," "shaved sides, natural top." Including hair type with comparable specificity to other feature descriptions ensures hair is a designed element of the subject's appearance rather than a default texture the model assigns arbitrarily.

Facial feature description should use the same vocabulary a portrait photographer or casting director would use — descriptive of the specific visual appearance of the feature rather than coding it as membership in a demographic category. Eye shape: "almond-shaped eyes with a slight upward tilt at the outer corner," "deep-set round eyes," "monolid with distinct natural fold," "hooded lid, prominent brow bone." Nose: "broad, flat bridge with wide nostrils," "high narrow bridge, slightly aquiline," "soft button nose, rounded tip," "prominent straight bridge." Lips: "full lips with a pronounced cupid's bow," "thin lips, well-defined philtrum," "medium-full with natural asymmetry." Jawline and face shape: "strong square jaw," "soft oval face, wide cheekbones," "heart-shaped face, prominent forehead." These specific descriptors produce distinctive, characterful human subjects rather than the averaged, blurred-ethnic-category faces that result from demographic shorthand labels. They also provide the model with enough precision to render features that read as natural and real rather than uncanny or composite.

Representing the full range of human age and body diversity requires intentional vocabulary because AI models default to generating subjects in a narrow age window and body type unless explicitly directed otherwise. Age vocabulary: "subject in their late sixties, natural grey hair, lived-in facial expression, laugh lines," "child approximately eight years old, gap-toothed smile," "woman in her mid-forties, natural fine lines beginning at eyes, no makeup." Body type vocabulary: "plus-size woman, size 18-20, confident posture," "lean and athletic build, visible muscle definition," "compact and stocky build," "petite frame," "tall and willowy, long limbs." Mobility and disability representation: "person using a manual wheelchair, active and engaged posture," "person with a visible below-knee prosthetic, standing in natural outdoor setting," "person using forearm crutches." Including these descriptors with the same specificity as any other subject attribute signals to the model that this is an intentional design decision, not an oversight — and produces imagery that reflects genuinely broader human experience.

Cultural context in clothing, jewelry, and setting can be included with intentionality without reducing characters to cultural stereotypes. The key distinction is between specific, accurate cultural description and reductive generic coding. Specific and accurate: "woman wearing a Yoruba aso-oke gele headwrap in royal blue and gold, traditional attire for a formal occasion," "man in a contemporary Korean fashion ensemble — oversized blazer, wide-leg trousers, minimal sneakers — Seoul urban setting." Reductive and generic: "African woman in traditional dress," "Asian man in his cultural outfit." The first type describes actual specific cultural practices and garments with respect; the second type collapses enormous cultural complexity into a generic label. When describing cultural context you are less familiar with, err on the side of less cultural specificity rather than risking an inaccurate or reductive depiction — describe the garment's visual properties (color, silhouette, fabric) rather than asserting a cultural label you cannot accurately characterize.

For brands and content teams producing imagery at scale, the most efficient approach to inclusive representation is to build a structured subject library in Floniks rather than writing diverse subject descriptions from scratch for each generation. The library is a collection of tested, validated subject description blocks — each specifying skin tone, hair, key facial features, age, body type, and styling — that can be quickly attached to any scene or product prompt. Aim for a library that covers a genuine range of the dimensions that matter for your audience: at minimum, six to eight skin tone variations, three to four hair texture categories, a range of ages, and varied body types. Test each library entry independently in /ai-image to verify it generates consistently and accurately before adding it to the library. In /editor, build a batch workflow that iterates through library subject entries and applies each to the same base scene prompt — this produces a diverse set of consistent scene variants in one run, ensuring your campaign imagery represents your audience without requiring manual prompt variation for each image.