How to Write AI Video Prompts That Don't Look AI-Generated
I started using AI video generation for product demos and social content about six months ago. The first results were unusable. Warping faces, melting objects, camera moves that looked like a drone having a seizure.
The tools have gotten dramatically better since then. But the gap between a good generation and a bad one still comes down to the prompt. The model has the capability — you just have to describe what you want in the language it actually responds to.
These are the techniques I've refined through hundreds of generations across Sora, Runway, and Kling. Grouped by the dimension of the video they control.
Camera Motion: The First Thing to Specify
If you describe a scene without specifying how the camera moves, the model picks a default. That default is usually a slow push-in or a static shot. Both look obviously AI-generated because real videos almost never hold perfectly still or drift forward for no reason.
Intentional camera movement
Camera: [movement type — slow dolly left to right / handheld with subtle shake / crane rising from ground level / locked-off tripod static]. Speed: [slow / medium / fast]. Duration: [time]. The camera [starts on / reveals / follows / pulls away from] [subject].
The "starts on / reveals" instruction is what gives the shot a purpose. A camera move without narrative intent looks like a screensaver. A camera that starts on a detail and pulls back to reveal the full scene looks like a filmmaker made a choice.
Matching camera to content type
Product showcase: smooth orbital motion around [object], 180-degree arc, constant speed, locked focus on product center. Surface reflections visible as camera moves.
Talking head: medium shot, subtle handheld micro-movements, slight push-in over [duration] to build intimacy. No lateral drift.
B-roll: slow tracking shot through [environment], camera at [height]. Gentle parallax between foreground elements and background.
I keep these as separate template blocks and paste the relevant one into each generation. The specificity of "subtle handheld micro-movements" versus "smooth orbital" versus "slow tracking" produces very different results. Generic "cinematic camera movement" produces generic output.
Temporal Consistency: Keeping Things Stable
The biggest tell of AI video is objects that morph between frames. A coffee cup that changes shape. A hand with flickering fingers. A logo that dissolves and reforms. These happen because the model generates each frame semi-independently.
Anchoring stable elements
[Subject] remains consistent throughout the entire shot: same proportions, same colors, same position relative to camera unless intentionally moving. No morphing, no warping, no sudden changes to [specific element you want preserved].
Explicitly naming what should NOT change is more effective than just describing what should. The model needs both the positive instruction (what to render) and the negative constraint (what to hold stable). I always call out the specific element most likely to morph — usually hands, text, or reflective surfaces.
Keeping text readable
On-screen text "[exact text]" in [font style], [color], [position on frame]. Text remains perfectly static and legible for the entire duration. No animation, no distortion, no partial rendering.
Text in AI video is still the hardest thing to get right. The "perfectly static" and "no partial rendering" instructions help, but honestly, I overlay text in post-production about 70% of the time. The prompt gets it right maybe 1 in 4 tries. When it works, it saves time. When it doesn't, I don't fight it.
Lighting and Atmosphere
Lighting language transfers almost directly from image prompts to video prompts. But video adds one dimension: lighting that changes over time.
Static lighting (most use cases)
Lighting: [source and direction — natural window light from camera left / overhead studio softbox / warm practical lamp in frame]. Consistent exposure throughout. No flickering, no sudden brightness shifts.
The "no flickering" instruction matters more than you'd think. Some models introduce subtle exposure fluctuations that look fine on a single frame but create a strobing effect in motion. Calling it out explicitly reduces the frequency.
Lighting transitions (advanced)
Scene begins in [lighting condition A — e.g., dim blue pre-dawn]. Over [duration], light gradually transitions to [lighting condition B — e.g., warm golden sunrise]. The transition is smooth and continuous. Shadow direction shifts naturally with the light source.
This is hard to get right but powerful when it works. The "shadow direction shifts naturally" instruction separates a real-looking time lapse from a simple color grade ramp. I only attempt lighting transitions in longer generations (4+ seconds) — shorter clips don't have enough frames for a smooth shift.
Motion and Physics
AI video models still struggle with realistic physics. Objects float, fabric doesn't drape naturally, liquids behave strangely. You can't fix this entirely, but you can reduce it.
Describing motion with physics cues
[Object] moves with [weight descriptor — heavy and deliberate / light and bouncy / fluid and smooth]. Affected by gravity. Momentum carries naturally — no abrupt stops or direction changes. Surface interaction: [describe — feet impact ground with slight compression, object slides with friction, fabric drapes with weight].
The "affected by gravity" instruction is surprisingly effective. Without it, objects in AI video often have a floaty quality — they decelerate unrealistically or hover slightly. Naming gravity as an explicit constraint grounds the motion.
Restricting motion complexity
Only [number] elements are in motion. Everything else is static or has only ambient micro-motion (slight breeze on fabric, subtle light shift). Primary motion: [describe]. Secondary motion: [describe]. No other movement.
This is the highest-impact instruction I've found for video quality. The more things you ask to move simultaneously, the more artifacts you get. Constraining motion to 1-2 elements and letting everything else sit still produces dramatically cleaner output. It's also more cinematic — real filmmakers isolate motion for emphasis.
Mistakes That Burn Your Generations
Describing a narrative instead of a shot
"A woman walks into a coffee shop, orders a latte, sits down, and opens her laptop" is a scene, not a prompt. That's 4 shots described as 1. The model will try to compress the entire sequence into a few seconds and the result will be a smeared mess.
Break it into individual shots. One prompt per camera setup. Edit them together in post.
Ignoring duration constraints
Every model has a sweet spot. Runway Gen-3 handles 4-10 second clips well. Longer generations introduce more drift. I prompt for the model's optimal duration and edit together, rather than pushing for one long take that degrades halfway through.
Skipping the negative constraints
[Full prompt]. Avoid: morphing, warping, extra limbs, jittering, sudden scene changes, watermarks.
Just like image prompts, explicitly stating what you don't want prevents the model from filling ambiguity with its worst tendencies. "No morphing" and "no jittering" are in every one of my video prompts now.
Making Video Prompts Compound
Video prompts are more complex than image prompts, which makes losing them more expensive. A good video prompt has camera motion, lighting, subject description, physics constraints, and negative instructions — rebuilding that from memory is painful.
I save every prompt that produces a usable generation. When I need a similar shot later, I start from the proven prompt and modify the subject, not the technical instructions. The technical blocks (camera templates, lighting setups, physics constraints) stay constant across projects.
That's exactly what Super Prompts is for. Save the prompt that worked. Tag it by shot type or project. Next time you need a product reveal or a B-roll tracking shot, pull up the template and swap in the new details.
Free to start. The next generation is the one where you stop recreating your best prompts from scratch.