Why video prompts are a different skill than image prompts
An image prompt describes one frame. A video prompt describes change over time β what moves, how the camera behaves, what happens first versus last, and increasingly, what it sounds like. That's a lot more to specify in one or two sentences, which is why the same prompting habits that work great for still images ("a golden banana on black marble, studio lighting") tend to produce flat, static-feeling video clips.
The fix isn't a longer prompt. It's a more structured one.
The five building blocks
Every strong video prompt on this platform touches five things, roughly in this order:
- Subject β who or what is on screen. Be specific about appearance, not just category: "a woman in a red trench coat," not "a person."
- Setting β where and when. Time of day and location do a lot of visual work for free: "a rain-slicked Tokyo intersection at night" tells the model far more than "a city."
- Action β what actually happens during the clip. Video models render motion, so give them motion to render: walking, turning, a hand reaching for a cup, steam rising.
- Camera language β how the shot is framed and how it moves (see the cheat-sheet below). This is the single highest-leverage addition most people skip.
- Style and lighting β the visual treatment: cinematic, handheld documentary, golden-hour, neon-lit, black and white film grain.
A minimal but complete prompt hits all five in one or two sentences: "A golden banana astronaut drifts past Saturn's rings, slow orbit camera move, cinematic lighting, wide shot, sense of scale and silence." Subject (banana astronaut), setting (past Saturn), action (drifting), camera (slow orbit, wide shot), style (cinematic, silence implies mood/sound).
Camera language cheat-sheet
Video models respond well to real cinematography vocabulary because it's well-represented in their training data. A few that reliably work:
- Orbit / arc β camera circles around the subject
- Dolly in / dolly out β camera physically moves toward or away from the subject (different from zoom, which stays put and changes focal length)
- Crash zoom β a fast, dramatic zoom in
- Static / locked-off β camera doesn't move at all, useful for letting action carry the shot
- Handheld β subtle natural shake, reads as documentary or found-footage
- Drone / aerial pass β sweeping overhead motion
- Rack focus β focus shifts from one subject to another within the shot
- Slow motion β self-explanatory, but naming it explicitly matters
Pick one, maybe two. Stacking five camera moves into a 5-second clip just confuses the result.
Planning around duration and aspect ratio
Every model on this platform has a fixed menu of clip lengths rather than an arbitrary slider, and it's worth knowing the menu before you write the prompt:
- Kling β 5 or 10 seconds
- Veo 3.1 β 4, 6 or 8 seconds
- Sora 2 β 4, 8 or 12 seconds
- Seedance β 5 or 10 seconds
Shorter clips reward a single, clean action β a product rotating, a hand reaching into frame, a gesture. Longer clips can carry a small sequence (someone walks in, sits down, picks up a cup) but will look better if you describe that sequence in order, since the model is effectively planning a mini shot list from your text.
Aspect ratio should match where the clip is going, decided before you generate rather than cropped after:
- 16:9 for YouTube, landing pages and traditional video placements
- 9:16 for TikTok, Reels and Shorts
- 1:1 for feed posts
Negative prompts: saying what shouldn't happen
Kling, Veo 3.1 and Wan on this platform accept a negative prompt β a separate field for things you want the model to actively avoid, rather than stuffing "no text, no watermark, no blur" into the main description where it competes for attention with what you actually want. Use it for recurring problems: warped hands, extra limbs, text artifacts, motion blur, or a style creeping in that you didn't ask for.
Image-to-video vs. text-to-video prompting
If you're animating an existing photo with Image to Video, drop the subject and style description β the image already defines both. Spend your entire prompt on motion only: "slow zoom in, steam rising from the cup, gentle camera sway." Redescribing what's already in the photo doesn't help and can actively conflict with what the model sees.
For pure Text to Video, you're defining everything from nothing, so the full five-part structure above matters more, not less.
Iterate like you mean it
Treat your first generation as a draft, not a final answer. A practical loop that works:
- Write a minimal prompt with all five building blocks, generate once.
- Look at what's wrong β usually the camera move, pacing, or a specific visual detail.
- Add or adjust one thing at a time rather than rewriting the whole prompt. If the motion is right but the lighting is flat, only touch the lighting language.
Prompting AI video is closer to directing than to writing a caption. The more precisely you can say what a real camera operator would do, the closer the output lands to what you pictured. For a side-by-side look at how each model actually performs on these prompts, see our Kling vs. Veo vs. Sora vs. Seedance comparison.