NotebookLM infographics: Custom prompts improve visuals, but spelling and hallucinations persist

What happened

A NotebookLM user (Obvious_King2150) posted a set of infographics on r/notebooklm and shared that the built-in infographic generator performs much better when fed custom prompts. The post includes several output images and a long comment listing nine different prompt-style briefs (Swiss minimalist, sketchnote, dark mode cyberpunk, vertical timeline, etc.). The community response was positive on aesthetics but repeatedly flagged two recurring issues, spelling errors and occasional gibberish or language-mismatch outputs. The Reddit thread is here: https://reddit.com/r/notebooklm/comments/1phccph/the_infographic_feature_works_much_better_with/

According to top comments

• “This just in: better prompts leads to better AI outputs.” (HeeHeeVHo) — sarcasm aside, prompts did materially change layout and style.
• Multiple users reported spelling mistakes in rendered text and limited editability of image text, making outputs hard to correct without manual image editing.
• One commenter (MajorBlink) saw about half the text turn into German or gibberish, suggesting language-model or locale mismatches for multilingual inputs.

What the post actually shows

• The author demonstrates that providing targeted style briefs yields clean, professional-looking infographic layouts.
• A high-value comment (score 60) lists concrete style prompts you can reuse (Swiss Minimalist, Technical Blueprint, Sketchnote, Cyberpunk Dark Mode, Student-friendly pastel, etc.). These are practical starting points for visual direction.
• Several comments note that the generated image text frequently contains spelling mistakes or mis-typed words (for example, “Congreas”), and that those errors aren’t trivially editable inside NotebookLM’s image output.

Why this matters

NotebookLM and similar infographic generator features promise quick visual synthesis of notes and documents, a valuable shortcut for students, researchers, and knowledge workers. However, when the generator embeds imperfect text into raster images, the UX breaks: you either accept noisy visuals or perform manual, time-consuming fixes in a separate editor. That undermines the time-saving pitch.

Context and likely causes

• Image-rendered text: many image-generation pipelines render text as pixels rather than vector text, causing spelling errors to be baked into an image.
• Model hallucinations or locale mismatch: the generator may pick the wrong language model for text generation, or combine layout and text generation in ways that amplify error rates.
• Non-editable outputs: NotebookLM appears to output final raster images without an easy “edit text” affordance, forcing post-processing.

Practical workflows and mitigations

1. Two-phase approach (recommended):
   • Phase A: Generate layout-only infographic (minimal or placeholder text).
   • Phase B: Generate the textual content separately as plain text using the same prompt, verify spelling and facts, then place vetted text into the layout using an image editor or a vector tool that supports text.
2. Reduce embedded text: keep infographic text short, fewer characters mean fewer mistakes.
3. Post-editing: crop and replace text areas with corrected text; users suggested tools like Nano Banano or standard image editors for fixes.
4. Test language settings: if you see German or gibberish, explicitly set language and locale in your prompt.
5. Reuse style briefs: copy the author’s style prompts (Swiss minimalist, technical blueprint, sketchnote, etc.) to get consistent aesthetic direction.

Key takeaway

The takeaway is straightforward: custom prompts make NotebookLM’s infographic outputs look better, but they don’t fix underlying reliability issues with generated text embedded as pixels. If you need a publishable infographic, treat NotebookLM as a rapid prototyping tool, not a finished product.

My takeaways from this post

1. Use custom prompts for style control, they work and save time when iterating layouts.
2. Always verify and, if possible, separate textual content from rendered images to avoid baked-in spelling and hallucinations.
3. Adopt a two-phase workflow: prototype visuals in NotebookLM, finalize text separately, then composite in an editor.

Remember that there is often another side to the story: the feature is improving quickly, and many users find the outputs “good enough” for drafts or study aids. For polished, shareable assets, expect some manual cleanup for now.