Three specific weaknesses come up constantly in discussions of AI image generation: hands with the wrong number of fingers or an anatomically implausible pose, text rendered as garbled, near-language shapes rather than actual readable words, and inconsistency in a character or object's appearance across multiple generated images. All three trace back to a related cause, worth understanding as one pattern rather than three separate quirks to individually remember.
For a current example or reference point in visual production, U.S. Copyright Office AI initiative provides additional context.
The shared cause behind all three
Hands are structurally complex, highly variable objects photographed and drawn from an enormous range of angles and poses in training data, without a strong, single, consistent pattern the way a face has — faces, by contrast, are photographed forward-facing constantly and share a highly consistent structure, which is part of why AI-generated faces are usually far more coherent than AI-generated hands. Text suffers from a related problem: the model has learned the general visual pattern of what text looks like — lines of small shapes with certain spacing and structure — without learning to actually spell, because it's fundamentally a visual pattern generator, not a language-and-visual-composition system unifying the two reliably. Consistency across images fails for a third, related reason: each generation is an independent process starting from fresh random noise, discussed in the ai-image-generation-whats-actually-happening guide elsewhere in this section, with no built-in memory of a specific character's exact appearance from one generation to the next.
What's actually improved, and what hasn't
It's worth being specific that meaningful progress has been made on all three problems across model generations — hands are noticeably more reliable in current-generation tools than in earlier ones, and some tools now render short, simple text reasonably well. None of the three problems is fully solved, and it's more useful to think of this as an ongoing, gradual improvement on a genuinely hard underlying problem than as a solved-versus-unsolved binary that shifts overnight with any single tool update.
For distributed teams applying these ideas in day-to-day operations, this online guide offers a related remote-work perspective.
- Expect hands, and other structurally complex, highly variable body parts or objects, to need more scrutiny and more likely regeneration than faces or simpler objects.
- Avoid relying on AI-generated text within an image for anything that needs to be accurate — add text separately in a design tool afterward for anything where the actual wording matters.
- For consistent character or brand appearance across multiple images, use a tool's specific consistency features (character reference images, fine-tuning on a specific subject) rather than expecting plain repeated prompting to hold appearance stable.
- Generate several variations and select the best rather than expecting the first result to handle a known-difficult element (hands, text, multi-subject composition) correctly.
- Check current, specific documentation for whichever tool you're using — the exact reliability of these three areas shifts meaningfully between tool versions, faster than general advice about the category can stay accurate.
- For a hand or text problem in an otherwise good image, targeted inpainting (regenerating just that region, discussed in the upscaling-and-editing guide elsewhere in this section) is usually more efficient than regenerating the whole image and hoping for a better result across the board.
Why understanding the pattern beats memorizing a list
A list of “things AI images are currently bad at” goes stale quickly as tools improve, but the underlying pattern — structurally complex, highly variable elements without a strong consistent training pattern tend to be less reliable than simpler, consistently-photographed elements — remains a useful lens for predicting where a new, unfamiliar tool is likely to struggle, even before you've tested it directly.
Working around these specific weaknesses — targeted editing for hands and text, dedicated consistency features for recurring characters — tends to produce better results than simply regenerating repeatedly and hoping the next attempt happens to avoid the problem.