AI video generation: turn a prompt or image into a short video

AI video generation is at roughly the stage image generation was at a few years ago: capable of genuinely striking results, unreliable in specific and learnable ways, and consistently oversold by the clips people choose to publish. Almost every impressive AI video you have seen is the survivor of many discarded attempts.

This is a practical guide: what these models can and cannot do, how to write a prompt that produces a usable shot, why length is the fundamental constraint, and how to assemble short clips into something worth watching.

Why clips are short, and why that is structural

Video models generate frames that must stay consistent with each other — the same face, the same shirt, the same room — while also moving plausibly. The computational cost grows sharply with duration, and so does the chance of drift: a face subtly changing, a background object appearing and vanishing, a hand becoming a different hand.

This is why practically every generator produces clips of a few seconds. It is not an arbitrary product limit, it is where quality falls off. Plan around it. The right mental model is that you are generating shots, not videos, and the video is what you make by cutting shots together.

Consistency across separate shots is the corresponding hard problem. Generating the same character twice, in two clips, and having them look like the same person is unreliable. The practical workarounds are to work from a single reference image, to shoot your character from behind or at a distance in some shots, or to design the piece so it does not depend on recognising a face across cuts.

The two ways in, and when to use which

Text-to-video takes a description and generates the shot. You get more variety and less control: composition, colour and framing are all decided by the model.

Image-to-video takes a still and animates it. This is the more controllable route and usually the better one. You can generate or choose exactly the frame you want — composition, subject, lighting all fixed — and then describe only the motion. It removes an entire category of disappointment, which is getting a well-animated clip of the wrong scene.

A workflow that works well: generate a still image, iterate on it until the composition is exactly right, then animate that image. You spend your iterations on the cheap, fast step rather than the slow, expensive one.

Writing a prompt for motion

The mistake most people make is describing a scene when they should be describing a shot. A video prompt has to say what moves and how.

• Name the camera move explicitly: slow push in, static locked-off shot, handheld follow, slow pan left, crane up. "Cinematic" is not a camera move and does nothing useful.
• Name the subject motion separately from the camera motion. These are different things and models conflate them if you do not separate them.
• Ask for one motion, not three. A shot where the camera pushes in while the subject turns and the light changes will fail at all three. Simple motions succeed.
• Say the speed. "Slow" is the single most useful word in video prompting — slow motion hides most artefacts, fast motion exposes them.
• Specify the lighting condition as you would for a photograph. It carries as much of the perceived quality here as it does in stills.
• Do not write what you do not want. The same negation trap as image generation applies: everything you name gets rendered.

What still goes wrong

Hands and faces in motion are the classic failures, and they are worse in video than in stills because you get many chances per clip to see one go wrong.

Text is unreliable. Signs, labels and captions in a generated clip typically shimmer or morph between frames even when the first frame reads correctly.

Physics is approximated, not simulated. Liquids, cloth, hair and anything with contact between objects are where the illusion breaks. A cup being set down on a table is a much harder shot than a cup sitting on a table.

Object permanence fails at the edges of frame. Things that leave the shot and return frequently return different.

Audio is usually absent or generated separately. Plan to add sound yourself; a silent clip reads as a technical demo, and a well-chosen ambient track does more for perceived production value than another generation attempt would.

Assembling shots into something watchable

Generate more shots than you need and expect to discard most of them. This is normal and it is how the impressive examples are made.

Cut on motion rather than between static frames — a cut that lands mid-movement hides discontinuity between two independently generated clips that a cut between two still moments would expose.

Keep individual shots short in the edit, shorter than the clip you generated. Artefacts accumulate toward the end of a generation, so trimming the tail is usually an improvement.

Add sound, and add it early. It is the cheapest available upgrade.

Label it as AI-generated if it is going anywhere it could be mistaken for footage of something real. This matters more for video than for stills, because video carries an intuitive assumption of having been recorded.

What it is realistically good for

Short social clips, background and ambience footage, concept and mood pieces, animated versions of stills you already have, and visualising an idea before committing a budget to filming it. Those are real uses and the technology is genuinely useful for them today.

It is not a replacement for filming something specific. If you need a particular product, a particular person or a particular place, film it. Generation gives you plausible, not accurate, and the gap between those two is exactly where the trouble is.

Video generation on RafayGen

RafayGen generates short video from the same chat as everything else — describe a shot, or upload or generate an image and ask for it to be animated. There is no separate app or separate subscription; it sits alongside chat, images and documents on one account, and prompts work in English, Urdu or Roman Urdu.

Video generation is a paid-tier feature, which is straightforwardly because it is the most expensive thing on the platform to run per request. Everything in this article applies to it, including the advice to iterate on a still first — it is much faster and it produces better clips.