How Engineers Use AI for Lightning Talks

AI is useful for ideation and structuring

But the core story must come from the speaker

For lightning talks, AI works best as an editor, not an author

In English-speaking communities, AI is often used for efficiency

In Japanese communities, AI is often used to clarify and verbalize rough ideas

Topic brainstorming

Outline generation

Title ideas

Slide copy

Diagram ideas

Speaker notes

Rehearsal questions

Translation and English polishing

Using ChatGPT or Claude to create outlines

Generating speaker notes and talk flow

Combining AI with Marp, Reveal.js, or Google Slides

Turning blog posts or README files into talks

Treating the result as “AI-assisted, human-edited”

Turning Qiita or Zenn articles into LT slides

Asking AI for better titles and openings

Reordering ideas into a more understandable flow

Shortening slide text

Creating English titles or abstracts

Compress it into a 5-minute talk

Add missing context for the audience

Break it into one message per slide

Find the core message

Suggest title candidates

Remove unnecessary details

Create a stronger closing line

Generate likely questions

Find points beginners may not understand

Detect risky or misleading wording

Check whether the talk fits within 5 minutes

Personal failure stories

Real engineering context

The messy reasons behind decisions

Team-specific atmosphere

Timing, pauses, and humor

The story becomes exaggerated

The argument becomes too safe

The slides feel generic

Personal experience disappears

The speaker ends up reading someone else’s words

Experience

Question

Failure

Friction

Conclusion

Structure

Compression

Rewording

Flow

Rehearsal

Clarify their intent

Avoid over-polished AI writing

Create lightweight Markdown slides

Work locally when needed

Preserve the speaker’s own voice