Why Your AI Images Look Generic (And How to Fix It with Better Adjectives)

We have all seen it. You are scrolling through a social media feed or reading a blog post, and you spot an image. It is high-resolution, the lighting is perfect, and the colors are vibrant. Yet, you immediately scroll past it with a thought: “That is definitely AI.”

There is a specific “glossy” look that plagues modern generative art. It is the phenomenon of the “average.” When you ask an AI to generate a “beautiful woman” or a “cyberpunk city,” it looks at millions of existing images in its training data and gives you the mathematical average of those concepts. The result is technically flawless but artistically hollow. It looks generic because it is, quite literally, the path of least resistance.

But here is the secret: the AI isn’t lacking creativity; you are lacking specificity.

The difference between a generic stock-photo lookalike and an award-winning digital artwork often comes down to a handful of powerful adjectives. To break free from the “default mode,” you need to expand your vocabulary. This guide will explore why generic images happen and provide you with the adjective toolkit to fix them.

The “Default” Trap: Understanding How AI Thinks

To fix the problem, you must first understand the mechanism. Text-to-image models are probabilistic. If you input a vague prompt like “a painting of a cat,” the model predicts the most likely interpretation. It usually generates a cute, fluffy cat looking directly at the camera with standard lighting. It plays it safe.

The more generic your words, the more generic the result.

However, advanced algorithms are capable of incredible nuance if you push them. Z-image interprets complex semantic relationships, meaning it doesn’t just recognize nouns (objects), but also understands the deep emotional and textural weight of specific adjectives. If you don’t utilize this capability by providing rich descriptive language, you are essentially driving a Ferrari in first gear.

To stop generating “average” art, you need to stop using “average” words.

The Banished List: Words to Stop Using

The first step to better imagery is to retire the words that everyone else is using. These words are “poison” for creativity because they are too broad.

Stop using: “Beautiful,” “Cool,” “Nice,” “Scary,” “Realistic.”
Why? “Beautiful” is subjective. To an AI, “beautiful” usually means “symmetrical face, smooth skin, soft lighting.” This creates the plastic, airbrushed look that is instantly recognizable as AI.

Instead of telling the AI that something is beautiful, tell it why or how it is beautiful.

Category 1: Texture and Tactility (Killing the “Plastic” Look)

The biggest giveaway of a generic AI image is the texture—or lack thereof. Everything looks too smooth, like it is made of digital plastic. To fix this, you need adjectives that describe how the surface feels.

For Portraits: Instead of “smooth skin,” try weathered, porous, freckled, scarred, grit, unpolished.
For Environments: Instead of “clean room,” try dilapidated, rustic, cluttered, dusty, sterile, polished mahogany, coarse concrete.

The “Touch” Test: If you can’t imagine what the image would feel like if you touched it, your prompt is too vague.

Generic Prompt: “A robot in a forest.”
Better Prompt: “A rusted, moss-covered automaton standing in a misty, overgrown forest, corroded metal texture.”

Category 2: Lighting and Atmosphere (Setting the Mood)

Generic images usually have “flat” lighting—everything is evenly lit so you can see it clearly. Artistic images use light to hide things.

While lighting warrants its own deep dive, specifically for avoiding the “generic look,” focus on adjectives that create contrast and imperfection.

Keywords to try: Chiaroscuro (strong contrast), moody, hazy, ethereal, harsh, fluorescent, gloomy, iridescent, volumetric.

An image with gloomy lighting is immediately more interesting than one with bright lighting because it implies a story. It invites the viewer to ask questions about what is hidden in the shadows.

Category 3: Camera and Lens Language (The Observer’s Eye)

If you don’t specify a camera style, the AI assumes a standard 50mm lens at eye level. This is the most boring angle possible. It looks like a passport photo or a standard product shot.

Force the AI to change its perspective using photographic adjectives.

Distance: Macro (extreme close-up), wide-angle, telephoto, aerial view, isometric view.
Focus: Bokeh (blurred background), shallow depth of field, sharp focus, motion blur.
Film Stock: Kodachrome, Polaroid, vintage film, Technicolor, grainy, sepia, bleached.

Adding “shot on Polaroid” to a prompt instantly removes the high-gloss digital sheen and adds a layer of nostalgia and imperfection that feels human.

Category 4: Art Styles and Mediums (Beyond “Digital Art”)

If you simply type “digital art,” you will get the standard ArtStation style that dominates the internet. It’s clean, it’s competent, and it’s forgettable. To stand out, mimic specific physical mediums.

Traditional Art: Impasto (thick paint strokes), watercolor, charcoal sketch, oil on canvas, gouache, ink wash.
Print Styles: Risograph, halftone, linocut, blueprint, woodblock print, etching.
Modern Styles: Glitch art, synthwave, low poly, brutalist, surrealism, Bauhaus.

Example:

Generic: “A picture of a cyberpunk city.”
Specific: “A Risograph print of a cyberpunk city, halftone patterns, vibrant pink and teal ink, rough paper texture.”

The “Uncanny Valley” Fix: Adding Imperfections

Paradoxically, the key to realism in AI is imperfection.

The generic AI look is too perfect. Teeth are too white; streets are too clean; skies are too blue. To make an image look authentic (whether photorealistic or artistic), you need to inject chaos.

Use adjectives that imply wear and tear:

Asymmetrical, cluttered, messy, chaotic, improvised, candid, accidental.

Instead of a “perfectly arranged living room,” ask for a “cluttered, lived-in living room with scattered books.” It feels real because it feels human.

Combining Adjectives for “Power Prompts”

The magic happens when you stack these categories together. However, be careful not to create a “word salad.” Aim for a cohesive vibe.

The Formula: [Subject] + [Texture Adjectives] + [Lighting Adjectives] + [Art Style/Camera Adjective]

Case Study: The “Warrior” Prompt

Level 1 (Generic): “A warrior wearing armor.”
- Result: A standard video game character concept. Shiny metal, neutral background.
Level 2 (Better): “A detailed warrior wearing dirty armor, cinematic lighting.”
- Result: Better, but still looks like a generic movie poster.
Level 3 (Specific): “A weary veteran warrior, dented and rusted iron armor, mud-splattered face, standing in heavy rain, desaturated colors, shot on 35mm film, grainy texture.”
- Result: An image that tells a story of battle, exhaustion, and history. It looks like a frame from a gritty historical drama, not a generic avatar.

Conclusion: Your Vocabulary is Your Limit

The AI tool you use is an engine of infinite potential, but it is passive. It waits for you to drive it. If you steer it down the middle of the road with common words, you will arrive at a common destination.

The “generic AI look” is not a limitation of technology; it is a limitation of language. By expanding your vocabulary to include words that describe texture, lighting, camera angles, and imperfections, you force the AI off the beaten path.

Next time you sit down to create, challenge yourself. Don’t just ask for a “forest.” Ask for a “dense, ancient, mist-shrouded forest with gnarled roots and dappled sunlight.” The difference between the two is the difference between a clip-art icon and a work of art.

Start building your adjective dictionary today. Your images—and your audience—will thank you for it.