Nano Banana 2: Preventing Hallucinated Ingredients on AI Labels

Understanding the Symptom of Fictional Text

When using Nano Banana 2 to generate packaging designs, a common issue arises where the tool creates plausible-looking but entirely fictional ingredient lists. You might see a bottle image featuring a label with complex chemical names that do not exist, or ingredients that are scientifically impossible for the product type depicted. This phenomenon is known as hallucination. The visual output often looks professional, with realistic typography and layout, which can be misleading. Users may assume the text is accurate because it appears so coherent within the context of the image. However, the presence of readable text does not guarantee factual correctness. In many cases, the AI has invented these strings to satisfy the visual requirement of a "label" without access to real-world formulation data.

It is crucial to distinguish between the visual aesthetic and the textual reality. The symptom is specifically the generation of non-existent or incorrect data within the text elements of the image. This is distinct from a blurry or unreadable font; the text is clear, legible, and grammatically structured, yet factually baseless. For users creating mockups for marketing or educational purposes, this can lead to significant confusion if the generated text is mistaken for a real product specification. The core problem is that the model prioritizes visual coherence over factual accuracy when rendering text.

Separating Plausible Causes from Known Facts

To address this issue effectively, we must separate the behavior of the tool from assumptions about its capabilities. A primary cause of these hallucinations lies in the fundamental nature of how the underlying models function. Google documents Nano Banana 2 as Gemini 3.1 Flash Image. While powerful, prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation. This means the model attempts to create the look of a label rather than retrieving specific data to populate it.

There is a common misconception that because the tool generates high-quality images, it possesses an internal database of real cosmetic formulations. This is not a fact. Nano Banana refers to the AI image generation/editing tool in these articles. It is not a skincare brand, bottle, jar, or physical subject. Example products are generic and unbranded. Consequently, the tool has no inherent knowledge of what ingredients belong in a specific lotion or shampoo unless explicitly provided in a reference image or prompt, and even then, it treats them as visual patterns rather than verified facts.

Furthermore, users should be aware of the limitations regarding different versions of the tool. Google describes Nano Banana 2 Lite as focused on speed and cost. It is not optimized for multiple reference inputs or multi-turn sequential editing. Do not recommend it for those workflows without explaining this limitation. If you attempt to use Nano Banana 2 Lite for complex labeling tasks requiring precise text control, the likelihood of generating inconsistent or fabricated details increases significantly due to these architectural constraints. The website has a Nano Banana Pro page at /nanobananapro, but its page named Nano Banana Lite at /nanobananalite does not by itself establish support for Google Nano Banana 2 Lite. Google model names and capabilities must not be presented as proof of availability or identical features on this website. Relying on the Lite version for critical text accuracy is ill-advised given its design focus.

Diagnosing and Fixing Label Accuracy Issues

Diagnosing the root of a hallucinated label involves a simple verification step: cross-referencing the generated text against known facts. Since the AI does not guarantee factual accuracy, the diagnosis is always positive for potential errors until proven otherwise. There is no automated setting within the interface to toggle "fact-checking" for text content. The only reliable method to fix these issues is manual intervention after generation.

The most effective strategy is to treat every piece of text in the output as a placeholder. When you generate an image, assume the ingredient list is completely made up. To fix this, you should not rely on the AI to correct itself through re-prompting alone, as it will likely just invent new fake words. Instead, use the image as a visual template. Generate the layout, lighting, and bottle shape you desire, but ignore the text. Once satisfied with the visual composition, take the image into a graphic design tool to overlay the correct, verified ingredient list. This ensures that the visual appeal of the Nano Banana 2 output is retained while the informational content remains accurate.

If you must keep the text within the AI workflow, you can try providing a very specific reference image containing the exact text you want, though success is not guaranteed. Prompt instructions describe desired outcomes; they do not guarantee identity, label, object or typography preservation. Therefore, even with a reference, the model may alter the spelling or order of words. For the highest reliability, external editing is the recommended path. You can explore more advanced features by visiting Try Nano Banana to see how different prompts affect the visual structure, but remember that text accuracy requires your own oversight.

Verifying Your Final Output

Verification is the final and most critical step before using any AI-generated label. Never publish or distribute an image containing an ingredient list without reading it line by line. Check for scientific impossibilities, such as ingredients that react negatively with each other or substances not approved for topical use. Look for nonsensical combinations that the AI might have created simply to fill space on the label.

Remember that the goal of this troubleshooting guide is to prevent content hallucination. By understanding that the AI is a creative engine rather than a data repository, you can manage expectations appropriately. Use the tool for its strengths: generating realistic textures, lighting, and bottle shapes. Delegate the responsibility of factual accuracy to human review. This approach ensures that your projects maintain both high visual quality and professional integrity. Always verify all text content manually since AI does not guarantee factual accuracy. By following these steps, you can leverage the power of Nano Banana 2 without falling victim to its tendency to invent information.