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A Practical Guide to AI Video Generation
26 August 2026 · 7 min read
Video generation has improved faster than almost any other area of AI, but expectations still run ahead of it. Knowing what the current generation of models handles reliably, and what it does not, saves a great deal of wasted time.
Think in shots, not scenes
The single most useful adjustment is to stop asking for scenes and start asking for shots. A scene implies continuity, multiple angles and narrative progression. A shot is one continuous camera view of one action, and it is what these models actually produce.
If you need a sequence, generate each shot separately and assemble them. Attempting to get a cut inside a single generation usually produces something incoherent, because the model has no reliable way to maintain identity and staging across a cut.
Describe camera and motion explicitly
Video prompts need everything an image prompt needs, plus movement. There are two kinds: what moves in the frame, and how the camera moves. Leaving either unstated invites the model to choose, and its default is often a slow drift that makes footage feel aimless.
State camera behaviour in the vocabulary of filmmaking. A locked-off shot, a slow push in, a handheld follow, a pan left to right. These are unambiguous and models respond to them well.
- Say whether the camera is static or moving, and how.
- Describe subject motion as one continuous action, not a sequence of events.
- Specify shot size: wide, medium, close-up.
- Keep the action simple enough to complete within the clip length.
Where these models still fail
Hands doing fine manipulation, text on signs and screens, and precise physical interaction between objects remain unreliable. So does anything requiring an object to remain exactly consistent as it moves through frame. If your shot depends on any of those, expect to generate many attempts or to reframe so the problem is out of shot.
Continuity across separate generations is the other persistent limitation. The same character described identically in two prompts will not come out identical. Where consistency matters, design around it: keep characters at a distance, use silhouettes, or accept that each clip is its own thing.
Length and pacing
Short clips are more reliable than long ones. Model coherence degrades over time, so a five second clip of one clean action is far more likely to be usable than a fifteen second clip of three actions. When you need duration, generate several short shots and cut between them, which is also how conventional filmmaking handles the same problem.
Match the action to the length. An action that would realistically take ten seconds will look rushed or will not complete if you ask for it in a four second clip.
Budget for iteration
Video generation has a much lower hit rate than image generation. Planning for several attempts per usable shot is realistic, and it changes how you should prompt: aim for one clearly specified thing rather than an elaborate composition where many elements must all succeed at once.
Magick Box offers several video models including Veo, Sora and Kling. They have genuinely different characters, and when a shot is not working the fastest fix is often trying the same prompt on a different model before rewriting it.