How to Make AI Photos Look Real Instead of Fake

There's a specific, recognizable look people mean when they say an AI photo looks “fake,” and it's rarely one big mistake. It's usually a handful of small physical inconsistencies stacked on top of each other — skin that's too smooth, eyes that don't quite focus on anything, lighting that doesn't match between the subject and the background. Fixing the biggest offenders individually does more than adding a general “make it realistic” instruction ever will.

Skin texture is the most common giveaway. Many models default toward smooth, evenly-lit skin because it's a safer average result. Asking explicitly for natural skin texture — visible pores, slight unevenness, natural texture rather than a retouched look — pushes the result away from that default and toward something that reads as a real photograph rather than a beauty filter.

Eyes are the second most common tell. Unnatural results often have eyes that are too symmetrical, too brightly lit compared to the rest of the face, or aimed in a direction that doesn't match the head's angle. Naming a specific gaze direction — looking slightly off-camera, or making direct eye contact with the lens — gives the model a concrete target instead of leaving it to guess.

Hands remain one of the harder problems for most image models, especially in poses where fingers overlap, hold an object, or rest against fabric. If hands are visible in your composition, an explicit anatomy instruction (“anatomically correct hands with natural finger placement”) measurably reduces the odds of an extra finger or an oddly bent joint, though it isn't a guarantee.

Lighting mismatch is a subtler problem that's easy to miss at first glance but registers instantly on closer inspection: a subject lit like they're indoors placed against a background that's clearly lit like outdoor daylight, or shadows falling in a direction that doesn't match the implied light source. Describing the light source once and its direction explicitly — rather than describing the subject's lighting and the background's lighting as if they're separate problems — keeps them consistent.

Reflections and shadows carry more weight than people expect. A mirror selfie with no visible phone reflection, or a person standing near a window with no corresponding shadow, breaks the illusion even when everything else in the frame is convincing. If your composition includes a mirror, glass, water or a polished surface, it's worth naming what that surface should realistically show.

Reference photo quality matters more than most people account for. A blurry, poorly lit or heavily-cropped reference photo gives the model less accurate information to preserve, and no amount of prompt refinement fully compensates for that. A clear, front-facing, evenly-lit photo is the single highest-leverage thing you can control before you even write the prompt.

Skin texture specified rather than left to default. Eye direction and expression named explicitly. Hands given an anatomy safeguard if visible. One consistent light source described for the whole scene, not the subject and background separately. Reflections or shadows addressed if the composition includes a mirror, window or glass surface. A clear, well-lit reference photo used as the starting point.

Resist the urge to add more realism keywords on top of each other — stacking “ultra-realistic, photorealistic, 8k, hyperrealistic” rarely helps and can push a result toward an over-processed look instead. Identify which single element looks wrong — skin, eyes, lighting, hands — and address that one thing specifically before touching anything else.

Not every concept should aim for photographic realism. A cartoon sticker, an anime portrait or a poster-style illustration is meant to look stylized, and pushing those toward photorealism usually makes the result worse, not better. Decide which category your concept belongs to before applying any of this — these fixes are for photo-style prompts specifically, not illustrated ones.

Every prompt in CreateLoom's photo-style categories is written with these specific details already built in, which is part of why identity and realism tend to hold up better than in a prompt assembled from a generic adjective list.