Generic prompts like “professional portrait of a woman” produce the same mannequin every time: symmetrical, plastic-skinned, lit from straight on. This tutorial gives you a 5-slot prompt scaffold and the lighting / lens vocabulary that gets headshots, character portraits, and marketing images that look photographed instead of rendered. Read this if you’re tired of generating 30 images to find one usable one.
What this covers
Realistic, flattering AI portraits built from five prompt slots (subject, expression, wardrobe, lighting, lens) plus the iteration discipline that keeps you from rewriting the prompt every time the face looks off.
Who this is for
Founders generating their own profile photo, creators making character references for fiction or games, indie marketers building avatars for landing pages, and anyone who keeps getting “uncanny valley” results from default settings.
When to reach for it
LinkedIn / X / About-page headshots, stylized character portraits for D&D or fiction, marketing avatars for testimonials (with consent), book-cover concepts, and dating-app or profile imagery where a real photoshoot is overkill.
Before you start
- Decide whether you want a real-photo look, a stylized look, or somewhere between — the prompt language differs sharply.
- Collect 2-3 reference images (a celebrity headshot, a stock photo, a painting) so you can name the look concretely instead of guessing.
- Pick aspect ratio up front: 1:1 for avatars, 3:4 for portraits, 9:16 for phone wallpapers. Switching mid-run wastes credits.
- Decide your reject threshold ahead of time — usually “1 keeper out of 8” is honest; “1 out of 30” means the prompt is wrong, not the model.
Step by step
- Start with the five-slot scaffold:
subject + age + expression + outfit + setting. Example: “woman, late 30s, soft smile with eyes engaged, charcoal blazer over white tee, soft-grey studio backdrop”. - Add lighting in plain photography terms — “soft window light from camera-left”, “golden-hour rim light”, “beauty dish above and slightly right”. Avoid abstract words like “cinematic” alone; pair them with a concrete source.
- Add lens + aperture + framing: “85mm, f/1.8, eye-level, shoulders-up”. The lens shapes face geometry — 35mm distorts noses, 85-135mm flatters.
- Add realism words last: “natural skin texture, visible pores, catchlight in both eyes, fine flyaway hair, sharp focus on iris”. Without these, defaults trend toward airbrushed.
- Generate 6-8 variants from the same prompt. Realistic portraits have higher reject rates than stylized art — never judge from a single output.
- Pick the best 1-2 and iterate by changing ONE slot at a time: only the lighting, only the expression, only the lens. Mixed changes mask which variable helped.
First-run exercise
- Pick one persona you actually need (your own headshot, a single character) — not a “portfolio” of ten different faces.
- Run the full five-slot prompt once and save the raw output. Don’t tweak yet.
- Mark each output as “usable, needs retouch, or reject” and write one sentence explaining why for each — this builds vocabulary fast.
- For the second pass, change only ONE variable (most often: lighting direction or lens length).
Quality check
- Does the face hold up at 100% zoom? Look for melted ears, mismatched earrings, asymmetric pupils, and floating hair strands.
- Are the eyes alive? Both irises should have catchlights from a believable light source — wax-eyed portraits read as fake within half a second.
- Is the skin textured, not airbrushed? Pores, faint shadows under the jaw, and color variation across the cheek separate “photo” from “render”.
- Does the outfit make sense for the lighting? A linen shirt in studio strobe looks wrong; a wool coat in golden hour also looks wrong.
How to reuse this workflow
- Save your winning prompt as a template named by purpose (“linkedin-headshot-male-40s”, “fantasy-character-rogue”) and only change the named variables next time.
- Build a small library of reject reasons: “wrong jawline”, “plastic skin”, “hand near face”, “iris asymmetric” — naming the failure speeds the next prompt.
- Re-test your template every 6-8 weeks; major model updates shift defaults and your “natural skin texture” trick may no longer be needed.
- Keep your reference image set with each template so you can recreate the look later, or hand it to a teammate.
Recommended workflow
Subject + expression + lighting + lens → first 8 variants → mark keepers → iterate one slot. If the first pass comes back with warped facial features, jump straight to the distorted-faces quick-fix pipeline instead of rewriting the prompt — most face issues are framing + resolution, not wording. For client / commercial use, route the winner through a light Photoshop retouch pass (skin balance, stray-hair cleanup) — that’s still faster than re-generating to perfection.
Common mistakes
- Plastic skin from missing texture words — defaults are tuned for “beautiful”, which translates to airbrushed; always include “natural skin texture, visible pores”.
- Wrong lens for the framing — 35mm at head-and-shoulders distorts the nose; use 85mm or longer for flattering portraits.
- Vague lighting like “good lighting” — name the source (“window light”, “softbox”) and the direction (“from camera-left”).
- Hands near the face — AI still struggles with finger anatomy; crop hands out or move them below frame.
- Judging from one output — realistic portraits demand 6-8 generations per prompt before you can fairly evaluate.
- Changing three variables at once — you’ll never know which fix actually worked.
FAQ
- Why does my portrait look like everyone else’s AI portrait?: You used default style words (“beautiful”, “professional”). Replace them with specific photographic language: lens, light source, and texture.
- Can I generate the same person twice?: Only loosely without a character-locking tool (reference image, LoRA, or model with identity controls). For consistent characters across scenes, see the character-motion workflow.
- My subject’s ethnicity keeps drifting — what fixes it?: Name the ethnicity directly in the subject slot and add specifics (“dark brown skin, deep-set eyes, broad nose”). Vague prompts default to the model’s average training subject.
- Should I retouch the AI output?: Yes, for any final use. Skin balance, stray-hair cleanup, and color grade take 5 minutes and lift the result above “obviously AI”.
- What aspect ratio for LinkedIn?: Generate at 1:1 (1024x1024) and crop to LinkedIn’s required ratio in the platform — generating at the exact final size sometimes truncates the head.