The silent layout bug in AI-generated slides
I frequently generate slides by summarizing documents or PDFs with AI tools. The workflow is fast and convenient, but one recurring problem kept showing up: parts of generated slides were silently cropped or overflowed their containers.
What made this issue dangerous is that slides often look fine during editing and review. The overflow becomes obvious only after export or during a live presentation. This article explains why these layout problems are easy to miss, why detectability matters more than repair in many cases, and how a simple heuristic tool can help catch layout overflows earlier in automated pipelines and manual workflows.
Why layout issues are easy to miss
Slide layouts depend on many moving parts: screen size, font rendering, code block wrapping, export targets, and viewer software. Small differences in any of these can cause clipping, hidden overflow, or line wrapping that changes the visual balance of a slide.
- Screen size and resolution: What fits on a laptop screen may overflow on an external projector or a different aspect ratio.
- Font rendering and metrics: Differences between platforms and font hinting can change line breaks and element height.
- Export format: HTML to PDF or PPTX conversions sometimes drop overflowed content or change wrapping.
- Dynamic content: Code blocks, tables, and images can expand beyond container bounds if not constrained.
Because these failures are often subtle, human reviewers can miss them. If a slide looks mostly correct, reviewers tend to move on. When slides are generated automatically, there is even less chance someone will spend time hunting for small layout flaws.
The real problem: detectability, not just fixing
After several embarrassing moments of clipped slides, I realized the core issue was not that layouts were hard to fix. The hard part was noticing failures early enough to matter. Without a reliable signal that something is wrong, both humans and automated systems will miss problems.
Creating a detection step that flags suspicious slides can change the workflow. Instead of relying on visual inspection after export, you get an automated check during generation or in continuous integration. That makes layout failures machine-detectable and actionable.
A small experiment: heuristic overflow checker
To explore this idea, I implemented a small command line tool that attempts to detect layout overflows in Slidev presentations. The tool is intentionally heuristic based and not perfect. The goal is not to guarantee correctness but to provide an early warning signal that something might be cropped or overflowing.
- Approach: Render slides in a headless environment, measure bounding boxes, and detect content that extends beyond visible container boundaries.
- Scope: Works well for Slidev and similar HTML-based slide systems; can be adapted to other generators with a rendering step.
- Limitations: Fonts, platform differences, and complex animations can produce false positives or negatives.
If you want to try the implementation, the repository is available at https://github.com/mizuirorivi/slidev-overflow-checker. The tool is meant to be integrated into CI pipelines, prepublish hooks, or local checks before exporting final slides.
Practical recommendations and checklist
- Integrate an automated overflow check into your build or CI process so failures are caught before export.
- Standardize fonts and test exports on all target platforms and aspect ratios used for presentations.
- Limit dynamic content sizes and set explicit overflow rules for code blocks, tables, and large images.
- Use visual regression tests where feasible to detect layout shifts between versions.
Takeaway: In AI assisted workflows, the absence of clear failure signals can be more limiting than generation quality itself. Making layout failures visible early transforms a silent bug into an actionable item and prevents surprises during a live presentation. I am interested in how others validate layouts and visual output in generated documents and slides.
For contributions or ideas, check the repository, open an issue, or share your approach to layout validation in automated pipelines.
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