What separates good from great

Great AI images look like they were composed by someone who decided what the image was going to be before they started. The subject is placed deliberately. The negative space is meaningful. The eye is led through the frame in a particular order. The rule of thirds is respected or violated for a reason. Good AI images, by contrast, feel like the model produced something passable and the artist accepted it. The subject is centered because that is where the model put it. The composition is competent but uninflected.

The way to develop compositional intent is to specify it in your prompts and to iterate aggressively when the model does not deliver it. Phrases like 'low angle wide shot with the subject in the lower-right third of the frame, dramatic empty sky filling the upper-left two-thirds' do real work, they tell the model what compositional choices you want, and modern frontier models (especially FLUX, MultimodalMultimodalHandling more than one kind of data, for example text and images together, in a single model. Diffusion TransformerTransformerThe neural network architecture built on attention that underpins modern language and, increasingly, image and video models. It scales well and handles long-range relationships better than convolutional networks. (MM-Diffusion Transformer (DiTDiT (Diffusion Transformer)A diffusion model architecture that uses a Transformer instead of a U-Net. Introduced by Peebles and Xie in 2022. Scales much better than U-Nets and is the basis for almost all current frontier models.))-based models, and language-model-native models like Nano Banana Pro) increasingly respect these specifications. The artists who care about composition spend much more time on the spatial language of their prompts than on the content language.

Light is what makes images feel real. It is also the dimension that base models handle most generically out of the box. Almost every default generation has 'soft, even, pleasant lighting' because that is what most photographs in the training data look like. Great practitioners specify lighting like cinematographers, they call out the direction, the quality, the color temperature, and the time of day.

The vocabulary that works includes phrases like 'hard sidelight from the left, deep shadows on the right side of the face,' 'overcast diffused daylight, no visible sun, cool blue-grey color palette,' 'practical light sources only, a single warm tungsten lamp,' 'golden hour backlight, subject in silhouette against the sun.' These are not generic promptPromptThe text description you provide to a model to specify what you want it to generate.-padding phrases. They describe specific lighting setups that the model has seen many examples of in training and can reproduce when asked correctly. The difference between asking for 'a portrait of a woman' and asking for 'a portrait of a woman, single key light from camera left, soft fill on the shadow side, dark moody background, color palette of deep teal and warm skin tones' is the difference between getting a generic stock photo and getting something that looks like it was lit by a working photographer.

Every surface in a great image has a specific material identity. The skin has pores. The fabric has weave. The metal has scratches. The wood has grain. Good generations get these roughly right, they look like skin, fabric, metal, wood, but they tend toward a generic, slightly plastic version of each material. Great generations push the model to be specific: 'aged copper with green patina,' 'fine merino wool with visible knit texture,' 'brushed stainless steel showing fingerprints,' 'reclaimed barn wood with deep grain and old nail holes.' The material vocabulary you bring to your prompts directly determines how tactile your outputs feel.

Great practitioners think about color as a palette, a small set of related hues that work together, rather than as a list of individual elements. The vocabulary is borrowed from color theory: 'analogous palette of teals and forest greens with a single warm accent,' 'split-complementary: dusty rose against deep green,' 'monochromatic earth tones, no saturation above 40 percent,' 'desaturated steel blues with one electric magenta accent in the foreground.' This level of specificity is rarely possible without conscious study of color theory, but the payoff is enormous, color discipline is one of the things that immediately distinguishes professional output from amateur output, and most amateur output fails on color before it fails on anything else.

Great images have detail that rewards close inspection. There is something interesting in the background. The clothing has visible texture. There are real objects on the desk that look like they belong there. Good images have detail at the focal point and noise everywhere else. The way to push for resolutionResolutionThe pixel dimensions of an image or video, for example 1024x1024. Higher resolution shows more detail but costs more memory and time. of detail is to be specific in the promptPromptThe text description you provide to a model to specify what you want it to generate. about what is in the secondary regions of the image, not just 'a man in a study' but 'a man in a study, books with visible spines on shelves behind him, an antique brass desk lamp casting warm light, an open notebook in the foreground with handwriting visible.' The model is capable of rendering these details; it just will not bother unless you ask.

If your image contains a person, the great version has a face that is unified, eyes that match, ears that align, hair that grows from a single scalp, a head that sits naturally on a neck. The good version has a face that passes a glance but breaks down on inspection. Most identity coherence problems are seed-and-samplerSamplerThe algorithm used to perform the denoising steps. Different samplers (Euler, DPM++, etc.) have different speed-quality trade-offs. problems and can be fixed by generating more samples with different seeds. Some are model problems and can be fixed by using a stronger model or adding a face-restoration pass. The artists who consistently produce great character work have a workflow for catching identity problems early and fixing them before the rest of the image is finalized.

Hands have improved dramatically since 2023 but they still fail more often than any other body part. The great version has five fingers per hand, in plausible configurations, with knuckles where knuckles should be. The good version is hopefully not showing hands prominently. The fix is some combination of: prompting explicitly for hands when they matter, generating multiple variations to find one where the hands work, inpaintingInpaintingEditing a specific masked region of an image while preserving the rest. The classic AI editing approach. just the hands as a separate pass, and using a hand-fix Low-RankRank (LoRA rank)The inner dimension of a LoRA's two small matrices, which sets how much capacity it has to learn. Higher rank captures more complex behavior but risks overfitting and produces larger files. Adaptation (LoRALoRA (Low-Rank Adaptation)A fine-tuning technique that adapts a base model to a specific style or concept using a small additional file. The standard way to customize open-source models.) on top. Working artists treat hands as a separate problem to be solved rather than something to hope for.

Great images look like they obey the laws of physics. Shadows fall in directions consistent with the light source. Objects sit on surfaces with weight. Liquids pool. Hair has gravity. Good images get these mostly right but have small inconsistencies that the eye picks up subconsciously, a shadow that goes the wrong way, a glass that doesn't quite touch the table, a strand of hair that floats. Physics inconsistencies are often the source of the 'something feels off about this' reaction even when the viewer cannot articulate what.

Great images look like they are in a particular style. Not 'cinematic.' Not 'professional photography.' A specific cinematographer's style. A specific era of fashion editorial. A specific painter's brushwork. The difference matters because 'cinematic' as a promptPromptThe text description you provide to a model to specify what you want it to generate. produces the average of all cinema the model has seen, which is generic and unsatisfying. 'Roger Deakins cinematography, deep shadows, single warm practical light source, anamorphic lens distortion' produces something that has a real visual identity. Specificity is a creative weapon.

Finally, great images have a clear emotional tone, calm, anxious, melancholic, triumphant, intimate, distant. Good images are emotionally neutral by default. Most beginners do not specify emotional register at all because they do not realize the model will respond to it. It will. Phrases like 'somber and quiet,' 'tense and electric,' 'wistful nostalgia,' 'serene and meditative' actively shape the model's output, mostly through the choices of color, light, and pose. The emotional tone is the thing that makes an image stick in someone's memory after they have seen it. It is often the last thing beginners learn to specify, and the first thing experienced practitioners think about.