What are the most common AI image prompt mistakes?

Overly chatty prompts, long or indecisive wording, contradictions, generic prompts across models, poetic language, misuse of negative prompts, wrong syntax, relying only on text prompting, and avoiding experimentation.

Should I write prompts like I talk to a chatbot?

No. Use direct, concrete visual descriptions—treat the prompt like a concise inventory of what should appear in the image.

How long should an AI image prompt be?

Be descriptive but efficient. Keep key concepts first and stay within encoder limits; many models truncate beyond ~50–200 words depending on the encoder.

Can I include alternatives like “this or that” in one prompt?

Avoid it. Image models try to include everything. Generate separate images for each option instead.

How do I use negative prompts correctly?

Place negatives in the proper field or syntax for your tool, describe what to avoid (not in the main prompt), and remember they gently steer results—they aren’t hard bans. Some models, like Flux, don’t support standard negative prompts.

Does prompt syntax differ across tools?

Yes. Midjourney uses flags like --no and :: weights; many Stable Diffusion UIs use parentheses for weights. Using the wrong syntax can change results.

What besides prompting improves results?

Combine techniques like inpainting, image-to-image, ControlNet, LoRAs, character and style references, and edit/instruct models for precise control.

10 Image Prompting Mistakes and How to Avoid Them

Writing prompts for AI image generators such as Stable Diffusion, Flux, or Midjourney is far more of an art than a science. Unlike language models, which are trained to follow instructions and reason through responses, image models remain somewhat enigmatic when it comes to crafting the perfect prompt.

While experimentation is valuable, there are several common mistakes that can significantly impact your ability to generate the images you envision. Here are ten frequent pitfalls and how to avoid them.

1. Prompting Like You're Talking to a Chatbot

Many users approach image generation with conversational language like "Please create an image of..." or "The image should contain elements such as..." This polite, instructional approach is counterproductive for image models.

Instead, focus on direct description. Simply describe the visual details you want the model to produce. Treat your prompt like a visual inventory rather than a conversation.

2. Writing Overly Long Prompts

Each image model can only process a finite amount of information, creating problems in two key areas: the text encoder and the model's processing capacity.

Text encoders convert your language into embeddings the model can understand, but they have strict token limits:

Flux: T5 encoder handles 512 tokens (approximately 375 words), while the CLIP-L encoder handles 77 tokens (about 50 words)
Stable Diffusion 3.5: T5 can encode 256 tokens (roughly 200 words), while CLIP encoders handle 77 tokens
Older Stable Diffusion models: Both CLIP-L and CLIP-G encoders handle 77 tokens (about 50 words)

Anything beyond these limits gets ignored. Be descriptive but efficient, and place the most important concepts at the beginning of your prompt.

3. Being Indecisive in Your Prompts

Prompts containing "this or maybe that" confuse image models. Unlike language models, image generators can't follow conditional instructions. Including multiple options tells the model to attempt incorporating everything, not to choose between alternatives.

Be decisive and specific about what you want. If you're uncertain between options, generate separate images for each choice rather than combining them in one prompt.

4. Creating Self-Contradicting Prompts

Contradictory elements like "a bald man with yellow hair" will still generate an image, but likely not what you intended. While contradictions can sometimes be used strategically to balance concepts, unintentional conflicts undermine your results.

Review your prompts for clashing concepts and ensure consistency throughout your description.

5. Using Generic Prompts Across Different Models

Every image model is trained on images with specific caption styles. The most effective prompts mirror the captions used during training.

Earlier models like Stable Diffusion 1.5 were trained on tag-based captions, requiring succinct, keyword-focused prompts. Modern models like SD 3.5, Flux, and DALL-E use more sophisticated captioning systems that understand natural, descriptive language.

Research your chosen model's training methodology and adapt your prompting style accordingly. Check sample images and prompts provided by the model creators to understand the expected format.

6. Writing Poetry Instead of Visual Descriptions

Language models often generate overly abstract, emotional prompts like "The image evokes a sense of curiosity and wonder, drawing the viewer in..." This poetic approach doesn't translate well to image generation.

Focus on concrete visual elements. Write as if describing a scene to someone who cannot see it. While abstract concepts like "curiosity" or "peace" might influence the overall mood, they should be minimal additions after establishing the core visual details.

7. Misusing Negative Prompts

Negative prompts can be powerful tools when used correctly, but they're frequently misunderstood:

Common mistakes include:

Describing unwanted elements in the main prompt ("a puppy with no collar" will likely generate a collar)
Using incorrect syntax (most models require separate negative prompt fields)
Attempting negative prompts with unsupported models (Flux doesn't support standard negative prompting)

Remember that negative prompts provide gentle guidance away from concepts rather than firm prohibitions. If your main subject is strongly correlated with the negative concept, it may still appear.

8. Using Incorrect Syntax

Every image generator has unique syntax for special features like prompt weighting or negative prompts. Midjourney uses "--no something" for negatives and double colons for emphasis ("hot:: dog::2"). Stable Diffusion interfaces often use parentheses for weighting ("(hot:2) dog").

Using the wrong syntax won't break generation, but the model will treat unsupported markup as part of the prompt text, potentially affecting results unexpectedly.

9. Relying Solely on Prompting

Prompting alone is often insufficient for achieving your creative vision. Many users get trapped in endless prompt refinement cycles, making incremental adjustments that never quite hit the mark.

Consider integrating these additional tools:

Inpainting: Mask and regenerate specific image portions with targeted prompts
Character references: Upload reference images for consistent character generation
Style references: Guide overall aesthetic through example images
LoRA models: Small, specialized models trained for specific styles or characters
ControlNet: Guide output using sketches, depth maps, or edge detection
Image-to-image: Use base images to influence structure and composition
Edit/Instruct models: Make targeted adjustments through text instructions

The most impressive AI-generated images typically combine multiple techniques rather than relying on prompting alone.

10. Avoiding Experimentation

While these guidelines provide a solid foundation, image generation remains more art than science. Sometimes unexpected combinations of seemingly contradictory concepts create surprisingly unique and compelling results.

Use these principles as starting points rather than rigid rules. Part of the creative joy in AI image generation comes from pushing models beyond their intended boundaries and discovering novel applications.

The key is balancing structured knowledge with creative exploration. Master the fundamentals, then let curiosity guide your experimentation.