10 tips for writing video prompts that actually follow your intent

You can write a beautiful prompt and still get a weird video.

The reason is simple: video models aren't just "image models + time." They're juggling camera motion, scene physics, identity consistency, temporal coherence, and (often) a tiny slice of the world knowledge you assume they have. That gap between what you mean and what the model can reliably execute is the whole game.

One research paper calls this the Intent-Execution Gap and points out that creators are pushed into "trial-and-error prompting" because single-shot generation is stochastic and under-specified for long-horizon work [1]. Another line of video research shows that even when you do specify camera behavior, it's hard for models to align generation with the camera trajectory unless the system is explicitly trained and rewarded for that alignment [2]. Translation: prompts need to be more like shot specs than vibes.

Here are 10 tips I use when I want a video model to do what I asked, not what it guessed.

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Tip 1: Start by locking the "deliverable"

Your first sentence should read like a production request, not a poem. Video prompting improves fast when you specify what the model is building: duration, aspect ratio, realism level, and the kind of output (single continuous shot vs. montage).

This maps cleanly to the idea in Vibe AIGC that high-level intent has to be decomposed into executable constraints instead of hoping the model "gets it" [1]. You're doing that decomposition manually.

A simple pattern I reuse:

Generate a single continuous 6-second shot, 16:9, photorealistic, natural motion, no cuts.

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Tip 2: Write the scene like blocking, not description

In video, "what's in frame" is less important than "what changes over time." So describe actions as verbs with a beginning and end state.

Good: "She turns her head toward the window and smiles."
Better: "She turns her head from camera-left to camera-right, eyes track the window, then a subtle smile forms."

This isn't just pedantry. Video models are trying to keep temporal coherence, and under-specifying motion is how you get jitter, teleporting hands, and unexplained camera jumps.

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Tip 3: Separate subject identity from wardrobe from environment

Identity drift is one of the most persistent failure modes. Even specialized editing/generation research spends a lot of effort on identity preservation across long segments [3]. Prompting can't fix architecture, but it can reduce ambiguity.

I'll literally label these in plain text (no fancy formatting required):

Subject: 30-year-old woman, olive skin, short black bob haircut, small mole under left eye.
Wardrobe: beige trench coat, red scarf.
Environment: rainy Tokyo street at night, reflections on asphalt.

If the model changes the subject, you now know what to reinforce on the next iteration.

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Tip 4: Treat camera direction as a first-class constraint

Most prompts bury camera motion at the end ("cinematic, slow zoom"). That's backwards. Camera is a control signal.

Camera-control research shows how hard it is to get true alignment even with dedicated conditioning and reward strategies [2]. In normal consumer tools, your best move is to be explicit and conservative.

Use unambiguous phrases like: "locked-off tripod," "handheld micro-shake," "dolly in," "orbit right 30 degrees," "pan left," "tilt down."

And pick one primary move. Stacking "dolly + orbit + crane" often produces mush.

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Tip 5: Specify continuity rules (what must not change)

A lot of "bad generations" are just continuity violations. You can preempt them with a continuity clause.

I like writing it as rules:

Continuity: same person throughout, same outfit, same background location, no face morphing, no extra people entering frame.

This pairs well with what the CamPilot paper calls "deterministic regions" vs. unconstrained areas in generation [2]. In prompting terms: you're telling the model what must remain deterministic.

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Tip 6: Use negative constraints, but keep them short

Negative constraints work, but a giant "no X, no Y, no Z" list becomes self-defeating. You want to ban the top 3 failures you actually see.

Community prompt analyses tend to echo this: negative constraints matter most when they're targeted (e.g., "no text, no watermark, no distorted hands") [4]. Use that as a practical heuristic, not gospel.

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Tip 7: Give the model a timing spine

If you care about narrative beats, don't trust the model to invent pacing. Give it timestamps.

0-2s: wide shot, she walks toward camera.
2-4s: medium shot, she stops under a streetlight, rain intensifies.
4-6s: close-up, she looks up, raindrops on eyelashes, slow blink.

This is the simplest way I know to reduce "random mid-shot changes," and it mirrors how real editors think: beats and cuts (even if you're asking for "no cuts").

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Tip 8: Don't ask for too many simultaneous "wow" factors

When you request photoreal, complex action, fast camera movement, perfect hands, readable text, and a brand-new character design… you're betting against the model's constraints.

The Vibe AIGC paper basically argues that single-shot prompting collapses under multi-dimensional intent and pushes you into reroll loops [1]. The practical takeaway is: choose a priority and sacrifice something else.

My rule: one hero element per prompt. If the hero element is "camera move," simplify everything else.

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Tip 9: Iterate like an engineer: diagnose, then patch

After a generation, don't rewrite the whole prompt. Patch the failure.

If the camera move is wrong, don't change wardrobe and lighting too. Tight loops beat big rewrites. This is especially important because generation is stochastic; you can confuse "random variance" with "prompt effect."

I usually run this loop:

Given my prompt and the result, identify the top 3 mismatches.
Rewrite ONLY the parts of the prompt needed to fix them.
Keep everything else unchanged.

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Tip 10: Use a "prompt compiler" template for consistency

If you're doing video often (product clips, ads, shorts), stop free-typing. Use a template with slots. It's basically the "prompts as functions" mentality people share in prompting communities, applied to video.

Here's a compact template you can paste into your workflow:

DELIVERABLE:
- Duration:
- Aspect ratio:
- Style/realism:
- One shot or multiple shots:

SUBJECT:
- Identity:
- Wardrobe:
- Props:

ENVIRONMENT:
- Location:
- Time of day:
- Weather/atmosphere:

ACTION (verbs + directionality):
- Start state:
- Motion:
- End state:

CAMERA:
- Framing:
- Lens feel (wide/normal/tele):
- Movement (one primary move):
- Focus behavior:

LIGHTING / COLOR:
- Key light:
- Contrast:
- Palette:

CONTINUITY RULES:
- Must stay constant:
- Must NOT happen (top 3):

TIMING (optional):
- 0-X:
- X-Y:
- Y-end:

This kind of structured intent is basically a tiny, manual version of what agentic orchestration papers suggest should happen automatically: take vibe-level intent and compile it into executable constraints [1].

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Practical examples (real prompts)

Here's a "clean" prompt that tends to behave:

Generate a single continuous 7-second shot, 16:9, photorealistic, natural motion, no cuts.

Subject: a golden retriever puppy with a blue collar.
Environment: cozy living room, morning sunlight through blinds, dust particles visible.
Action: puppy starts sitting, then stands up and trots toward a tennis ball, nudges it once with its nose, then looks back to camera.
Camera: low angle at puppy eye-level, slow dolly in, shallow depth of field, background soft bokeh.
Lighting: warm soft sunlight, gentle shadows, no harsh contrast.
Continuity: same puppy throughout, collar stays blue, no extra animals, no text, no watermark.

And here's a product-style workflow idea people actually use: generate a strong still first, then animate it with an image-to-video model, then assemble in an editor [5]. The prompting implication is: write prompts for shots, not "make me an ad."

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If you try only one thing: write your next video prompt as if you were handing it to a cinematographer who can't read your mind. Because the model can't.

References
Documentation & Research

1. Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestration - arXiv - https://arxiv.org/abs/2602.04575
2. CamPilot: Improving Camera Control in Video Diffusion Model with Efficient Camera Reward Feedback - arXiv - http://arxiv.org/abs/2601.16214v1
3. EditYourself: Audio-Driven Generation and Manipulation of Talking Head Videos with Diffusion Transformers - arXiv - http://arxiv.org/abs/2601.22127v1
4. Introducing Trusted Access for Cyber - OpenAI Blog - https://openai.com/index/trusted-access-for-cyber

Community Examples
5. "tried a bunch of ai video tools for social media and here is what worked." - r/PromptEngineering - https://www.reddit.com/r/PromptEngineering/comments/1qw4k2p/tried_a_bunch_of_ai_video_tools_for_social_media/
6. "After analyzing 1,000+ viral prompts…" - r/PromptEngineering - https://www.reddit.com/r/PromptEngineering/comments/1qq4tet/after_analyzing_1000_viral_prompts_i_made_a/