If you've spent any time playing with AI art tools, you already know the drill: you type a prompt, hit generate, and get something that looks... okay. Maybe a bit plastic. Maybe the hands are wrong. Maybe it looks like a stock photo from 2009. The gap between "AI generated" and "actually looks real" comes down to a handful of habits that most people never bother to learn.
Over at aiimagegenerators.net we see thousands of prompts a week, and the same mistakes show up again and again. Here's what actually moves the needle.
Stop Writing Sentences, Start Writing Specs
The single biggest upgrade is treating your prompt like a brief for a photographer, not a wish you're making at a genie. Instead of "a woman in a coffee shop," try "a 30-year-old woman with curly auburn hair, sitting at a small marble cafe table, window light from the left, shallow depth of field, 50mm lens look."
Notice what changed. You described a subject with specifics (age, hair texture, hair colour), a location with detail (small marble table), a lighting direction (window light from the left), and a camera quality (50mm, shallow depth of field). Lighting and camera language do more heavy lifting than any style word you can add.
Lighting Words Are Your Best Friend
If you only memorise one category of vocabulary, make it lighting. These phrases consistently produce better results:
- Golden hour — warm, low sun, forgiving on skin
- Overcast soft light — even illumination, great for portraits
- Rim light — separates your subject from the background
- Practical lights in background — bokeh, night scenes, mood
- Hard directional light — drama, contrast, editorial feel
Mixing two light sources usually beats one. "Soft window light from the left, warm lamp glow behind" reads like a real room, and the model tends to render real rooms better than abstract ones.
Composition Cues the Model Actually Understands
Terms like "close-up portrait," "medium shot," "wide establishing shot," "low angle," and "over-the-shoulder" are borrowed straight from film production. Models trained on captioned photography pick up on them reliably. Combining a shot size with a lens look and a lighting phrase gives you a complete visual recipe in about fifteen words.
When to Use Negative Prompts
Negative prompts are useful but people overdo them. Throwing forty things into a negative prompt muddles the result. Pick the two or three artefacts you're actually seeing — blurry, extra fingers, warped text, plastic skin — and list only those. Iterate: generate, look, add one more only if the problem persists.
A Simple Workflow That Works
Start with a plain description of your subject and scene, nothing fancy. Generate four variations. Pick the one whose composition you like, then rewrite the prompt adding lighting, lens, and mood language, and regenerate. Finally, change one variable at a time to refine — swap the background, adjust the time of day, change the colour palette. Changing five things at once teaches you nothing because you can't tell what caused the improvement.
What Actually Wastes Your Time
Cranking up style modifiers like "8k, ultra detailed, masterpiece, trending on artstation" does very little on modern models. They were a workaround for older architecture and mostly add noise now. Likewise, stuffing in the names of five different artists produces a muddy blend rather than a coherent style. One artist reference, if any, is plenty.
If you want to see how these habits play out in practice, you can try the AI image generator yourself and compare a bare-bones prompt against a fully specified one side by side. The difference is usually obvious within a dozen generations, and once you've seen it you won't go back to one-line prompts.
Keep a running notes file of phrases that worked. Prompting is a craft, and the people who get consistently good output are the ones who kept notes.
Getting Aspect Ratio Right Before Anything Else
Choosing the wrong aspect ratio wrecks otherwise good prompts. A cinematic 21:9 frame with a portrait subject leaves them tiny and lost. A square frame can crop off hands you deliberately posed. Decide what the image is for before you generate: square for social profiles and thumbnails, 4:5 or 3:4 vertical for phone wallpapers and portrait subjects, 16:9 for anything that reads as a scene. Generating in the correct ratio from the start beats cropping, because cropping throws away the composition the model built for you.
Save Prompts That Work, Version Them
The most underrated habit is keeping a personal prompt library. When a generation comes out particularly well, paste the exact prompt into a notes file with a short label like "studio portrait, soft window light, muted palette." Within a month you'll have twenty reliable recipes you can adapt rather than rebuild. Treat each one as a template — swap the subject, keep the lighting and lens language. This is how working illustrators use these tools, and it's why their output looks consistent from image to image.
Seed and Reproducibility
Most tools expose a seed value, which is the random number that drives generation. If a tool lets you reuse a seed, you can hold everything constant and change exactly one word in your prompt to see precisely what that word does. This turns prompting from guesswork into a controlled experiment. It's slower per attempt, but you learn far faster, and you can reproduce a favourite image months later instead of hoping you still have the file.
Common Beginner Traps Worth Naming
A few patterns reliably produce disappointment. Writing the entire prompt as a comma-separated list of adjectives with no subject produces a texture rather than a scene. Describing the impossible — "a photo of a conversation" — produces confusion, because there's no single frame that contains a conversation. Asking for readable text in the image is still unreliable on most models, so plan to add text in an editor afterwards rather than fighting the generator. And describing a famous character by name gets inconsistent results, since the model has many conflicting references; describing their features works far better.