Why your AI image Model Hallucinates and How to Fix it with this Simple Grounding Trick

One of the biggest headaches in the age of AI is "hallucination." This common pitfall of large language models (LLMs) causes AI to generate convincing but completely made-up responses. For example, if you ask an LLM to cite a specific legal case to support an argument, it might invent a case name, a plausible-sounding judge, and a completely fabricated ruling.

While we often discuss hallucination in the context of text, AI-powered image generators face the exact same problem. If you ask an image model to create a subject that wasn't well-represented in its training data, it might start "guessing," resulting in something either completely different or only vaguely similar.

For instance, when I asked SeeDream (a popular image model) to draw the European Central Bank (or even just "ECB"), it understood it was a building and had a general idea of a large structure. However, the result was far from the actual building:

In some situations, this might not be a big deal. If you're creating a creative ad where a slight deviation from the real object doesn't matter, this might be sufficient.

However, in other scenarios, it's simply not an option. For educational purposes or businesses with real products, when an AI-generated Nike shoe has only a "quite similar" icon or logo, it's a deal-breaker.

Frankly, even if it's not business-critical, I see a problem with the distortion of reality in people's minds. For example, if the pyramids are badly "guessed" by all image models, only those who have actually visited the pyramids will have a true understanding of this amazing monument with all its facets. But that's a different discussion.

In this blog post, I'll share how well (or poorly) common image models actually depict our physical world. More importantly, I'll show you how to avoid image hallucination using a simple trick that works with "AI image editing models" like Nano Banana and SeeDream.