Why Your AI Images Come with Errors—And How to Improve Them

AI-driven text-to-image generation models have shaken up digital art and content creation, enabling any user, regardless of their background, to produce high-quality, customizable visuals with just a few words in a fraction of the time it would take a human professional using classic design or photo tools. 

With powerful technological advancements, AI-assisted creativity is becoming  increasingly integral to workflows across various industries. However, creating a commercial-ready piece with AI isn’t about pushing a magic button, as its ‘voilà’ effect does not always deliver usable results, especially for those relying on it to meet professional artistry and design standards. 

In reality, while mastering prompt writing—the language AI understands—is the primary condition for achieving output that aligns with one’s creative vision, AI-generated imagery may still present some common frustrating flaws, affecting not only beginners but also seasoned creators. Overcoming these issues often requires additional knowledge and skills from both users and developers.

Below, I’ll outline the most frequent challenges in AI image generation and share practical solutions to work around them.  

Prompt Engineering Complexity 

The core appeal of AI image generation is transforming ideas into visuals in nearly an instant using just words. However, the complexity of prompt engineering is yet one of the most significant barriers to producing meaningful images. Even slight variations in wording can lead to drastically different outputs. Prompt structures may also vary across models, so what works well in one may produce poor results in another. This lack of standardization in prompt language often forces users to go through trial and error. 

Prompt libraries and databases help reduce the guesswork by providing pre-tested prompts that users can reference or modify as needed. Visual prompt builders enable users to input keywords in a structured manner, select attributes, adjust sliders, and more, making the process of crafting an effective prompt more intuitive. Learning from successful prompts shared by the community is also valuable, as these real-world examples demonstrate what works.  

To improve consistency, standardized prompt syntax guides suggest best practices for structuring keyword inputs across different models. Using prompt templates promotes more predictable results, helping users generate multiple images with a consistent style. Emerging models like FLUX are more user-friendly overall, as they are designed to be less sensitive to prompt complexity, allowing users to create coherent, complex scenes from more straightforward instructions.  

Anatomical Inaccuracy 

Due to how neural networks learn from datasets, diffusion models don’t actually understand anatomy—they generate images based on pattern recognition rather than a structured biological framework. For example, AI doesn’t view a hand as a composition of five distinct fingers that can articulate differently. Instead, it blends statistical averages seen across training images. As a result, deviations from expected poses or angles can cause distortions. While modern models have improved significantly, abnormalities like extra fingers, unnatural face and body proportions, unrealistic limb connections and joint placement, or asymmetrical and misaligned eyes remain common. 

Fine-tuning models with LoRas (Low-Rank Adaptation technology) focused explicitly on anatomical datasets helps them develop a more comprehensive understanding of human structure. ControlNets, particularly those utilizing pose estimation or edge detection (such as Canny filters), enable AI to adhere to anatomical guidelines. 

Prompts that specifically reference realistic body details can also improve the anatomical accuracy of generated figures. Post-processing with anatomy-aware correction tools allows users to fix flawed areas without regenerating the entire image. 

Identity Inconsistency Across Multiple Generations 

Since AI treats each generation as an independent process, maintaining a consistent character appearance across multiple images remains a challenge, particularly problematic for storytelling or series-based artwork where character continuity is crucial. Even when using the same prompt, subtle changes in facial features, clothing, or style can appear between renders. The issue may get even more pronounced in batch generations, where quality and visual traits fluctuate unpredictably.  

Training a LoRA on a set of images of a specific person or object, and using a reference image as input, can improve identity conditioning, consistency, and uniformity. Embedding techniques and adapters (like PuLID, IPAdapter, InstantID, and EcomID) help preserve character traits across generations. When facial accuracy is critical, face swap models or post-processing offer a more tailored refinement, ensuring key features remain identical from generation to generation. 

Background Incoherence 

AI-generated backgrounds are prone to unrealistic, structurally and contextually incoherent design, making the images look less believable. For example, perspective may feel off, or lighting and shadows may not match the subject. This occurs because diffusion models perceive the background as a secondary element rather than an integral part of the scene, resulting in issues with depth perception, object correlation, and environmental context.  

Depth mapping helps models interpret spatial relationships more accurately, facilitating a more realistic integration between the foreground and background. Perspective guides enforce geometric alignment, helping keep architectural structures and vanishing points consistent. Focused relighting LoRas can learn to generate lighting and shadows along with the background, ensuring the reflections behave naturally throughout the scene. 

Fine-tuning models on datasets featuring specific settings (like urban landscapes, nature scenes, or interior spaces) can improve overall background realism. Reference background images will also help anchor the generation to real-world compositions.

Text Rendering Issues 

Trained primarily on visual data, not structured language, AI struggles with generating legible words and phrases within the image. The text may appear incomplete, gibberish, jumbled, or nonsensical, with irregular fonts or misaligned placement. When readable, it may still look stylistically off or awkwardly blended into the background. 

Unlike humans, most AI models don’t recognize text as distinct from surrounding elements, so they don’t process it as a separate entity. Instead, they treat character sequences as another visual pattern featuring abstract shapes rather than meaningful semantic symbols. 

To improve text rendering quality, researchers train models on specialized text datasets containing properly labeled typography examples that help AI better understand letter formation, alignment, and spacing. Text-aware masking is another effective technique when blank areas are reserved for text during image generation, allowing for cleaner integration during post-processing. 

Lack of Control Over Output 

While the results can be visually impressive, a significant limitation of AI image generation stems from the lack of precise control over the final output. Users may struggle to direct the model toward specific styles, ensure realism, or tweak fine details. Other common errors include unexpected elements in the scene, ambience-disrupting colors, and layout inconsistency. Unlike human artists, who adjust with intent, AI operates probabilistically, sometimes yielding surprising or undesired results. 

Control mechanisms, such as ControlNets and LoRas, allow users to condition structure through pose, depth, or edge guidance. For more precise aesthetic steering, custom models trained on particular styles can significantly enhance coherence in artistic direction. Additionally, referencing a specific image through image-to-image generation helps maintain the relevance of the output.

Masking and inpainting tools enable editing specific parts of an image without affecting the rest. Post-processing tools, such as upscalers and enhancers, can add the final polish to AI outputs by enhancing resolution and clarity. 

Overall, AI has yet to develop a more sophisticated and nuanced prompt interpretation—a challenge that remains one of the central ones to maintaining control. Many models tend to overinterpret instructions, attempting to extract deep or layered meanings where they are not intended. While this sounds intelligent, even a detailed prompt can produce unpredictable results. For instance, AI may emphasize or invent unexpected elements based on the associations it has learned. It increases the complexity of prompt crafting, requiring users to adapt to how the model “thinks” (which isn’t always intuitive) and spend more time experimenting with wording to achieve the desired outcome. 

Final Thoughts 

Understanding how AI interprets visual data—and recognizing where it tends to fall short—allows for making smarter choices in prompt writing, employing effective problem-solving strategies, and selecting the right tools to work around occurring generation errors. Ultimately, it empowers users to work with AI as a creative partner rather than relying on luck or viewing its technical limitations as deal-breakers in creating usable content that accurately reflects the creator’s vision. 

Originally Appeared Here