How I Learned to Write Better Nano Banana Image Prompts (and Where I Find Real Examples)

When I first started using Nano Banana, I quickly realized that what you write matters just as much as what you imagine. If you leave your prompts vague or unclear, you often get results that miss the mark — like backgrounds you didn’t want, odd lighting, or composition that feels off. That’s something a lot of prompt guides will tell you too: clarity is key, and being specific helps the model understand your vision better rather than guessing at abstract concepts.

Over time I learned a few practical patterns that make a real difference when writing prompts for image generation:

1. Be specific about your subject and style.

Simply saying “a portrait” is too broad. Including details like lighting, mood, and visual style gives the model more to work with. For example, specifying “soft morning light” or “dramatic lighting with vibrant colors” can refine the result significantly.

2. Add composition and atmosphere.

AI models don’t inherently know what we mean unless we tell them. Explicitly including terms like “close-up,” “wide shot,” or “central composition” helps generate a more structured result.

3. Avoid ambiguity.

Clear language tends to produce clearer images. Phrases with double meaning or vague descriptions can lead to unexpected results, so straightforward instructions usually work best.

4. Iterate and refine.

A single prompt rarely produces perfection on the first try. I’ve learned to think of prompt writing as an iterative process — look at what was generated, identify what could be improved, and tweak a specific part of the description. This trial-and-error approach helps you learn what works and what doesn’t.

5. Learn from examples.

There’s no substitute for seeing real prompts that have already produced results you like. Early on I struggled with this because most collections I found either had only text or lacked visual examples, making it hard to guess how a prompt would perform.

That changed when I found a site called Banana Prompts. What I like about it is that the prompts are shown alongside images they produced, so you can immediately see what kind of output a given prompt leads to. It made the learning curve much shorter, and I could directly compare wording with results to understand how small changes affect outcomes.

👉 [https://bananaprompts.fun](https://bananaprompts.fun)

If you’re serious about improving your Nano Banana prompt game, focusing on clear, specific prompts and learning from real examples goes a long way. Once you start paying attention to structure and how each phrase influences the result, you’ll find your images become more intentional and closer to what you envision.