A Visual Notebook for Testing Nano Banana Pro AI Images
A few notes from testing AI-generated images where the words inside the image matter as much as the mood.
I like image generators most when they leave behind something I can inspect. Not just a polished picture, but a draft with enough clues to ask: is the title readable, is the scene still coherent, and did the model invent anything that looks too confident?
Nano Banana Pro AI caught my attention for that reason. Google describes Nano Banana Pro as Gemini 3 Pro Image, with stronger support for text in images, diagrams, infographics, mockups, and controlled edits. That is a useful direction, but it also raises the bar for checking the result.
The first-pass question
Before I judge whether an image looks good, I want to know whether it survives a smaller, less flattering view. If the image becomes a thumbnail, can the main words still be read? If the color mood changes, does the subject stay the same? If a label appears, is it actually spelled correctly?
This is the small checklist I keep next to the prompt:
one main subject
one short readable phrase
enough quiet space around the subject
no fake logos or brand-like marks
no factual claims that have not been checked elsewhere
A prompt that behaves like a note
The prompt I would start with is plain on purpose:
Create a 16:9 editorial image for a visual notebook post. One main subject. A short readable title. Soft evening color. No fake logos. No tiny unreadable text. Leave quiet space around the subject.
After that, I would change one thing at a time. A different background. Then a shorter title. Then a tighter crop. If everything changes at once, I cannot tell whether the model improved the image or simply wandered into a different one.
The test page I used
For this note, I used Fylia Nano Banana Pro AI page as a browser-based test surface:
Free to create images & videos all in one place with Fylia AI. Upload images and transform into stunning images/videos with top advanced AI
The page shows image upload, prompt input, translate and prompt optimization controls, ratio and resolution choices, and a generation history area. That makes it useful for a small loop: generate, inspect, revise, compare.
I would not treat a single interface as proof of model quality. It is more like a worktable. The useful part is whether the same test can be repeated without losing the prompt, the reference image, or the reason for the next edit.
Where it feels useful
The strongest use case is not a finished factual document. It is the early visual draft: a blog header, a zine cover idea, a small explainer graphic, a mock product layout, a recipe card draft, or a poster concept that still needs human review.
I would be careful with anything that looks official: medical diagrams, legal explainers, financial charts, maps, public notices, or multilingual instructions. Those can begin as image drafts, but the final facts should come from checked sources and manual review.
Small FAQ
Is Nano Banana Pro AI only about text inside images?
No. Google also describes it for image editing, diagrams, mockups, infographics, and controlled visual generation. Text is only one part of the bigger workflow.
Can I trust the generated words?
No. Treat them as draft text. Check spelling, numbers, names, and any factual detail before publishing.
Why use Fylia AI in this note?
Because it gives a visible browser workflow for testing Nano Banana Pro AI-style image generation. Here it is used as a reference worktable, not as a ranking or endorsement.
Closing note
The part I care about most is not whether the first image looks impressive. It is whether the draft remains readable, checkable, and revisable after the first reaction passes.
That is the difference between a pretty output and a usable visual draft.
