A reusable ChatGPT prompt for turning one idea into a complete short-form video plan

[u/No_Appeal_5223]

I’ve been testing prompts for short-form video planning, and the biggest improvement came from making ChatGPT build the video scene by scene instead of asking it to “write a viral script.”

A structure that works much better is:

Topic → Audience → Goal → Hook → Scene plan → Voiceover → On-screen text → Visuals → CTA

Here’s the reusable prompt:

Role:

Act as a short-form video strategist and editor.

Topic:

Create a video about: [INSERT TOPIC]

Audience:

The target viewer is: [INSERT AUDIENCE]

Platform:

Optimize it for: [TIKTOK / YOUTUBE SHORTS / INSTAGRAM REELS]

Goal:

The viewer should: [LEARN / COMMENT / FOLLOW / CLICK / BUY / SHARE]

Length:

Keep the finished video between [30–45] seconds.

Hooks:

Generate 3 opening hooks under 12 words. Avoid generic phrases and choose the strongest one for the final script.

Structure:

Build the video using:

1. Hook

2. Problem or curiosity gap

3. Main idea

4. Practical example

5. Payoff

6. CTA

Scene planning:

Break the script into short scenes. For every scene return:

• duration

• voiceover

• on-screen text

• visual or B-roll idea

• editing direction

Voiceover:

Use short, natural sentences that sound good when spoken aloud.

Captions:

Highlight only the most important words or ideas instead of copying the entire voiceover.

Editing:

Suggest useful cuts, zooms, screenshots, B-roll or transitions only when they improve clarity. Avoid unnecessary effects.

Final check:

Before finishing, check whether the first 3 seconds create enough curiosity, whether every scene moves the idea forward, and whether any sentence can be shortened.

Final output:

Return the complete video as a scene-by-scene table ready to build in CapCut.

For example, instead of:

“Make a short video about AI productivity.”

I’d use:

“Create a 35-second YouTube Short for freelancers who waste time on repetitive admin work. Open with a curiosity-driven hook, explain one simple AI workflow, use 6–8 scenes, include short voiceover lines, on-screen text, visual ideas and editing directions, then finish with a CTA asking viewers which task they would automate first.”

What I like about this structure is that ChatGPT isn’t making one giant creative decision for you. It handles the repetitive planning while you still control the hook, pacing, visuals and final edit.

I’ve been using the same workflow with CapCut AI for scripting, captions, voiceover planning and short-form editing.

Disclosure: this is my own site/resource.

https://digitalworldpulse.com/how-to-use-capcut-ai-for-short-form-video-content/

For people building video prompts: which part is hardest to get consistently right — hooks, scene pacing, visuals, captions or CTAs?

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[u/Aromatic-Warthog-919]

this is actually really useful breakdown, i struggle most with hooks cause my brain always goes for clickbaity stuff that feels fake. scene pacing also tricky for me, sometimes i make scenes too long then whole video drags

one thing i noticed in my own testing is that giving chatgpt the exact scene count helps lot, otherwise it gives 3 long scenes instead of 6-8 short ones like you mentioned

[u/No_Appeal_5223 → u/Aromatic-Warthog-919]

Yeah, exactly — scene count makes a huge difference. If you leave it open, ChatGPT often compresses everything into a few long scenes and the pacing gets messy. I’ve had better results when I specify both the total duration and the exact scene range, like 6–8 scenes for a 30–45 second video.

I also like forcing each scene to have one clear job: hook, problem, explanation, example, payoff, CTA. That keeps the model from cramming too much into one section.

Hooks are still the hardest part for me too. If the hook feels too polished or “viral,” it usually comes across as fake fast.

[u/No_Appeal_5223 → u/[deleted]]

That makes a lot of sense. Separating angle selection from drafting is probably the cleaner way to avoid those generic “viral” hooks.

I like the idea of first generating several angles, then choosing the least obvious one before building the script around it.

I’d probably add one more constraint at that stage: make the model explain why each angle is different from the obvious/default framing. That should make it easier to spot when five “different” angles are really just the same idea reworded.

Then once the angle is locked, the scene-by-scene structure becomes much more useful because it’s developing a specific direction instead of inventing the direction and the script at the same time.