Most people who get mediocre results from a text-to-image model aren't writing bad prompts. They're writing short prompts and hoping the model fills the gaps with good taste. It usually doesn't. It fills them in with whatever it saw most often in training, which is why so many outputs look like the same stock photo with different subjects.
The fix isn't a longer prompt. It's a more specific one. Below is the structure I use when generating on aiimagegenerators.net, and why each part earns its place.
Name the light before the subject
Light is the highest-leverage detail in any image, and it's the one most prompts skip. "A woman in a bakery" gives the model nothing to work with. "A woman in a small bakery, morning light coming through a window on her left, soft shadows across the counter" gives it a direction, a quality, and a time of day.
Three words cover most of it: direction (left, backlit, overhead), quality (soft, hard, diffused), and source (window light, overcast sky, a single lamp). You don't need all three every time, but you need at least one. Backlit hair alone will do more for a portrait than five adjectives about beauty.
Be specific about scale and quantity
Models default to a middle distance with the subject filling the frame. If you want a wide establishing shot, say "wide shot" and give it something to establish. "A wide shot of a fishing harbor at low tide with nine boats pulled up on gravel" is a picture. "A beautiful harbor" is a mood board.
Numbers help more than people expect. "Three chairs," "a stack of four crates," "two windows." They stop the model from doing its default thing of scattering objects evenly and vaguely around the frame.
Choose one style and commit
The most common failure I see is stacked styles: "cinematic, photorealistic, illustrated, watercolor feel." Every one of those is a different rendering engine in the model's head, and the result is a smooth, uncommitted in-between that looks like neither.
Pick one lane. Documentary photograph. Editorial product shot. Painted illustration. Then use the rest of your prompt to make that lane convincing, instead of hedging across three. If you want to explore alternatives, run the same prompt with only the style line swapped and compare. That's a controlled experiment, and it teaches you far more than regenerating the same prompt ten times.
Reserve the space you need
If the image is going to carry text, a headline, a price, or a logo later, say where the empty space should be. "Subject in the lower right third, plain sky in the upper left" is a compositional instruction, and most models respect it. Generating a perfect subject dead center and then discovering you have nowhere to put a headline is avoidable.
Same logic for crops. If the image will be used as a square thumbnail, generate square or wider and crop in. Generating a tall portrait and then cropping to a square usually beheads someone.
Say what you don't want
Some tools have a separate negative prompt field; others only have the main box. Either way, naming the specific thing you want excluded is worth more than a list of generic quality words. "No text, no watermark, no extra fingers, plain background" does real work. "No bad quality" does nothing, because the model has no clear idea what you mean by bad.
Iterate one variable at a time
When a result is close but not right, resist the urge to rewrite the whole prompt. Change one thing (the light, the distance, the background) and keep everything else identical. You'll learn what the model actually responds to, and you'll end up with a prompt you can reuse across a project instead of one lucky output you can never reproduce.
Keep the seeds and the prompts that work. A folder of three proven prompts is worth more than a hundred screenshots of things you liked once.
Two habits that save time
The first is writing the prompt as a sentence a photographer would say, not a keyword list. "A woman reading at a kitchen table, late afternoon sun from the right, half the table in shadow" reads naturally and covers light, subject, and composition in one go. Comma-separated keyword piles work, but they tend to produce images that feel assembled rather than seen.
The second is naming the camera position. "Shot from slightly above," "eye level, close," or "looking up from the ground" changes the whole feeling of a scene and costs you four words. Most flat-looking generations are flat because the model defaulted to eye level and a mid-range distance, which is the least interesting place a camera can be.
Neither habit requires a better model. Both require you to say what you actually want instead of hoping it's implied.
The short version
Light first, then scale, then one committed style, then the space you need, then the exclusions. That order maps to how much each element changes the result. If you're starting fresh, spend a few minutes on the generator running the same subject with only the lighting line changed. You'll see more improvement in five generations than in fifty random re-rolls.