Fixing Inconsistent Capitalization in Nano Banana 2 Business Cards

When generating professional assets like business cards, the visual presentation of text is critical. A single misplaced lowercase letter in a name or title can undermine the perceived authority of the brand. Users of Nano Banana 2 have reported instances where the model generates short text blocks with inconsistent capitalization, such as random lowercase letters appearing in proper nouns or erratic casing in job titles. This behavior stems from the inherent challenges AI image models face when rendering specific typography within complex compositions.

It is important to distinguish between known facts about the tool's capabilities and plausible causes for these errors. Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image). While this model supports text-to-image workflows, prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. The symptom of inconsistent capitalization is not a bug in the code, but rather a limitation in how the model interprets strict typographic rules during generation. Unlike a word processor, the image generator creates pixels based on patterns, which can lead to deviations from standard English capitalization rules when the text is small or integrated into a design.

Distinguishing Symptoms from Model Limitations

The primary symptom users encounter is the appearance of names or titles that fail to adhere to standard capitalization rules. For example, a user might request "John Smith, Senior Manager," only to see the output render as "john smith, senior manager" or "John smiTh, SeNior MaNaGer." This issue is particularly prevalent in short text blocks where the model has less context to infer the correct structure.

Plausible causes often include the complexity of the background design or the specific phrasing of the prompt. However, it is a known fact that prompt instructions do not guarantee typography preservation. The model prioritizes the overall aesthetic and composition over strict orthographic accuracy. This is distinct from the limitations of Nano Banana 2 Lite, which is focused on speed and cost and is not optimized for multiple reference inputs or multi-turn sequential editing. If you are using a different version, the likelihood of maintaining precise text control may vary significantly.

Understanding that the AI is creating an image, not editing a document, helps manage expectations. The model attempts to mimic the look of text rather than processing it as data. Consequently, even with clear instructions, the final pixel arrangement may occasionally deviate from the requested casing. This is a characteristic of the underlying technology rather than a failure of the user's input quality.

Diagnosing the Root Cause of Typographic Errors

To diagnose why your business card mockup contains erratic casing, consider the interaction between your prompt and the model's constraints. Since Nano Banana 2 does not guarantee typography preservation, the model may struggle to maintain case sensitivity when the text is rendered at a small scale or when the font style is highly stylized.

Another factor to consider is the specificity of the prompt. While prompts describe desired outcomes, they are not binding contracts for every character. If the prompt relies heavily on implicit assumptions about formatting rather than explicit descriptions, the model may fill in gaps with its own training data, leading to inconsistencies. Additionally, if the design includes complex elements surrounding the text, the model might prioritize the visual balance of the entire image over the grammatical correctness of the words.

It is also worth noting that while the website hosts a Nano Banana Pro page and mentions other variants, the availability of specific features must be verified against the actual product capabilities. Do not assume that all versions of the tool handle text with equal precision. The core issue remains that the AI generates images based on probability distributions, which can result in occasional spelling or casing errors that would not occur in traditional graphic design software.

Practical Steps to Fix and Verify Text Accuracy

Addressing inconsistent capitalization requires a combination of refined prompting and post-generation verification. Start by being explicit in your prompt instructions regarding capitalization. Instead of simply stating the name, describe the format: "Use Title Case for the name and Job Title." While this does not guarantee the outcome due to the nature of image generation, it provides stronger guidance to the model.

If the initial generation fails, try regenerating the image with slight variations in the prompt wording. Sometimes, rephrasing the request to emphasize the professional nature of the text can help steer the model toward more accurate results. You can also explore the prompt library offered by the platform to see how other users have successfully structured similar requests. Remember that these examples are untested prompts provided for inspiration and should be adapted to your specific needs.

Once you generate an image, verify the text carefully. Zoom in to check every letter. If the capitalization is incorrect, the most reliable fix is often to regenerate the image with adjusted parameters or to use external tools to edit the text layer after generation, as the AI cannot always be trusted to get it right on the first try. For users seeking a more robust solution for complex editing tasks, exploring other options within the ecosystem might be beneficial, though each tool has its own strengths and limitations.

For those ready to experiment with these techniques and refine their business card designs, Try Nano Banana offers a dedicated space to test these workflows. By understanding the limitations and employing strategic prompting, you can minimize errors and produce high-quality professional mockups that meet your standards.

Always remember that while AI is a powerful creative assistant, human oversight remains essential for ensuring the accuracy of professional materials. Regularly checking your outputs against your original requirements will help you catch any inconsistencies before finalizing your designs.