How to Write Video Prompts That Actually Direct the Camera (Not Just Describe a Vibe)

Most "video prompts" I see are really image prompts with extra adjectives. They describe a scene, maybe a mood, and then hope the model invents the cinematography, continuity, and motion. That's why outputs often look like: pretty frames… stitched into confused motion.

Here's the thing: video generation is a control problem. You're not just telling the model what exists. You're telling it what changes, what stays consistent, and how the camera behaves over time. And the fastest way to level up is to stop writing prompts like poems and start writing them like a shot list with rules.

I'll show you a structure that works across most text-to-video and image-to-video tools. It's model-agnostic on purpose. The details vary, but the underlying constraints don't.

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The mental model: prompts are contracts across time

A good video prompt is a contract with three parts.

First, a contract about identity (who/what must remain the same from frame to frame). Second, a contract about physics and motion (what moves, how fast, with what trajectory). Third, a contract about camera behavior (what the viewer sees and how that view changes).

Research backs up why this matters. In OmniTransfer, the authors show that video diffusion models can preserve consistency better when the model can leverage context and structured cues rather than relying on "figure it out" text alone. They even demonstrate a failure mode where the model can keep things consistent when content is arranged side-by-side, but struggles when the same action must remain consistent across sequential shots-because temporal consistency is hard and needs help from how context is represented [2]. You don't need their architecture to benefit from the lesson: when you ask for multiple beats, you must explicitly anchor continuity.

Another useful takeaway comes from work on prompt evaluation in LLM systems: prompt quality improves when you treat prompts as testable artifacts, iterate with templates, and compare variants systematically instead of vibe-based tweaking [1]. Video prompting benefits from the same discipline, even if your "judges" are your own eyes and a small set of acceptance criteria.

So let's write prompts like contracts.

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The core structure I use (and why)

I think of a video prompt as six lines. Not bullets in the final prompt necessarily, but six slots you should fill.

You start with Intent. One sentence. What is this clip for? Ad, explainer b-roll, short film, UI demo, music visualizer. This matters because it sets tradeoffs: do you want clarity or art, realism or stylization, speed-ramping or steadiness.

Then Subject + invariants. Name the subject and lock the parts that must not drift. Clothing, age, facial features, brand assets, background location, time of day. If continuity matters, I'll say it plainly: "same person throughout; no outfit changes; keep logo legible."

Then Setting + time. Where are we and when is it? "Rooftop at blue hour, light wind, city bokeh." Don't overdo it. Video models can drown in detail. Pick what the audience will notice.

Then Action beats. This is where most prompts fail because they ask for "a cinematic shot of X" but never define the motion. I like to write beats as a tiny timeline: what happens at the start, midpoint, end. If it's a 5-second clip, you can still describe it as: "starts with… then… ends with…"

Then Camera choreography. Your camera is either locked, handheld, dolly, crane, drone, POV, macro, etc. You also need framing (wide/medium/close), lens feel (wide angle vs telephoto), and movement path. This is the difference between a clip and a slideshow.

Finally Output constraints. Duration, aspect ratio, style, realism level, and the "do not" rules that actually matter. I keep this short and concrete. If you add ten negative constraints, the model will ignore half of them anyway.

That's the whole game: define continuity, define motion, define camera.

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The catch: don't ask for multi-shot unless you really mean it

This is my spiciest take: beginners shouldn't ask for "Scene 1 / Scene 2 / Scene 3" until they can reliably produce a single coherent shot.

Why? Because multi-shot implies identity persistence across discontinuities, changes in framing, and temporal logic. OmniTransfer highlights exactly this kind of challenge: sequential shot consistency is fragile, and models can "lose the thread" between segments [2].

If you need multi-shot, you'll get better results by generating separate single-shot clips and cutting them in editing. Or by using tools that support storyboards, shot chaining, or reference video conditioning.

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Practical prompts you can copy and adapt

Here are a few prompt patterns I actually use. They're written to be pasted into most video generators, but they also work well when you're asking an LLM to draft prompts for a specific tool.

1) Product b-roll: "clarity beats cleverness"

Create a 6-second realistic product b-roll video for a landing page.

Subject: a matte-black wireless earbud case on a clean white desk. Keep the same object throughout. No logo distortion.

Setting: soft window light from the left, subtle shadows, minimal background.

Action: the case slowly rotates 90 degrees clockwise on the desk. The lid opens halfway, pauses, then closes.

Camera: macro close-up, shallow depth of field, smooth slider movement left-to-right, no jump cuts.

Constraints: 16:9, stable exposure, realistic materials, no extra objects entering frame, no text overlays.

2) Character moment: "lock identity, then animate emotion"

Generate a 5-second cinematic single-shot video.

Subject: a woman in her 30s with short curly hair, green raincoat, small scar on left eyebrow. Same person throughout.

Setting: nighttime street, light rain, neon reflections on wet pavement.

Action: she looks down at a buzzing phone in her hand, exhales visible breath, then looks up with a small relieved smile.

Camera: medium close-up, 50mm feel, gentle handheld micro-movement, slow push-in.

Constraints: realistic, consistent face, no scene change, no wardrobe change.

3) Motion study: "say what moves and what stays"

Create a 4-second video of a skateboard rolling through frame.

Subject: a red skateboard with worn grip tape. Keep board design consistent.

Setting: sunlit concrete skatepark.

Action: skateboard enters from the left, rolls to center, performs a small ollie, lands, exits right. Background stays static.

Camera: wide shot, locked-off tripod, natural motion blur.

Constraints: 24fps look, realistic physics, no camera movement.

If you want a "real-world" habit that helps, a lot of practitioners say they learn faster by collecting full back-and-forth chats and iteration trails, not just saving a single final prompt. That resonates with my experience too: the prompt history teaches you which constraints actually bind the model [3].

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How I iterate without going insane

I run video prompting like a tiny experiment.

I start by defining what "success" means in three checks: subject consistency, camera behavior, and the single most important action beat. Then I change one thing at a time.

If the subject drifts, I tighten invariants and remove decorative adjectives. If the motion is wrong, I simplify the action beats and make them more physical ("moves 1 meter forward," "rotates 90 degrees," "hand raises to eye level"). If the camera is wrong, I stop describing the vibe and name the rig ("locked tripod," "slow dolly-in," "drone orbit").

This is basically prompt evaluation thinking applied to video: treat prompts as designs you can compare, not as spells you recite [1].

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Closing thought

If you remember one thing, make it this: you're not prompting for a picture. You're prompting for a transformation over time.

Write the transformation. Write the camera. Lock what must not change. Then iterate like an engineer, not a gambler.

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References

Documentation & Research

1. LLM Prompt Evaluation for Educational Applications - arXiv (The Prompt Report) - http://arxiv.org/abs/2601.16134v1
2. OmniTransfer: All-in-one Framework for Spatio-temporal Video Transfer - arXiv (The Prompt Report) - http://arxiv.org/abs/2601.14250v1
3. PVH reimagines the future of fashion with OpenAI - OpenAI Blog - https://openai.com/index/pvh-future-of-fashion

Community Examples
4. How do you study good AI conversations? - r/PromptEngineering - https://www.reddit.com/r/PromptEngineering/comments/1qp7get/how_do_you_study_good_ai_conversations/
5. prompts - r/ChatGPTPromptGenius - https://www.reddit.com/r/ChatGPTPromptGenius/comments/1qo4lbi/prompts/