The Complete AI Video Prompting Guide (2026)

TL;DR: This is the map, not another deep dive. Prompt anatomy, per-model guides for Veo, Kling, Sora, Seedance, Runway, Luma, Grok Imagine, Wan, LTX, Vidu, Pika, HeyGen and Hailuo, camera and motion control, duration and audio, continuity, and the troubleshooting posts for when a clip looks fake or won't move, all in one place, each linked to its own full write-up.

The complete AI video prompting guide (2026)

If you searched for an AI video prompting guide, you have probably already found one of the dozens of deep-dive posts this site publishes on the topic, camera vocabulary, per-model syntax, duration limits, sound design, and more, and landed here looking for how they fit together. This page is that map. Each section below covers one piece of the picture in a few sentences and links straight to the post that goes deep on it, so you can either read this page top to bottom for the full shape of AI video prompting, or jump straight to the one section you actually need.

What you will not find here is a restated version of any of those posts. If a page already publishes the model's parameter table, the exact multi-shot grammar, or the 30-term camera glossary, this page names it and links it rather than rebuilding it. That keeps this guide honest about what it is: a router with enough of its own substance to answer the basic version of your question on the spot.

This is written for two different readers, and both should be able to stop reading the moment they have what they need. If you have never written an AI video prompt before, start at the anatomy section below and follow the model table to whichever vendor matches your job. If you already have a working prompt and something specific is going wrong, camera behaving oddly, audio not showing up, a character drifting between clips, skip straight to the matching section further down; each one names its own failure modes rather than assuming you need the whole guide.

What's actually inside a video prompt?

Every model reads roughly the same shape: who or what is in frame, what happens and in what order, how the camera behaves, and what the scene looks and sounds like. Where models diverge is in exactly which of those become prompt text versus a separate parameter, and how rigidly each one enforces its own syntax.

A minimal skeleton that works as a starting point on almost any model looks like this:

Subject: [who/what is in frame, key visual details]
Action: [what happens, in the order it happens]
Camera: [shot type and any movement]
Style/Lighting: [look, mood, time of day]
Audio: [dialogue, sound, or "silent" if the model defaults to audio]
Duration: [seconds, if the model takes it as prompt text rather than a parameter]

That six-line version is deliberately bare. For the full seven-part breakdown, with worked examples of what goes wrong when a part is missing, see our anatomy of a video prompt guide. For the more cinematic version of the same idea, framed around shooting a scene rather than filling in fields, see how to direct AI video like a filmmaker.

Most first prompts fail not because a field is missing but because it's vague where it needed to be specific. "A woman walks through a city" leaves the model to guess the era, the weather, the pace, and the shot; "a woman in a rain-slicked trench coat walks briskly through a neon-lit Tokyo crosswalk at night, tracking shot at eye level" removes most of those guesses. The skeleton above is where you check which field you left vague before you touch a model-specific parameter.

Which model should you actually prompt?

Model choice comes before syntax, because the thing you are trying to make usually rules out half the field before you write a word. Here is what each model documents itself as being for, and where to go for the full prompting guide.

If you are choosing between the two most-searched options, our Veo 3 vs Sora vs Kling comparison and our best AI video prompt tools roundup both rank on what each vendor documents rather than on tested picture quality, which nobody on this site has measured. For quick starting points rather than a full framework, we also publish free generators for Veo 3 and Kling 3.0.

How do camera, motion, and lighting control the shot?

Camera and motion language is the part of a video prompt that carries over best across models, because it describes cinematography, not an API field. A push-in, a slow pan, a static locked-off frame: those words mean roughly the same thing whether you're prompting Veo or Kling.

Our camera movement vocabulary covers 30 terms with what each one actually changes in the output, and 30 cinematic camera prompts for Veo 3 and Kling puts that vocabulary into full worked prompts. Kling specifically also exposes a dedicated camera control panel and a Motion Control feature for transferring movement from a reference clip, covered in our Kling camera and motion control reference and Kling Motion Brush guide.

How long can a clip be, and what does that cost you?

Duration is the setting most likely to silently reject your prompt, because on several models it is coupled to resolution in ways that are easy to miss until a request fails. Our AI video duration parameters reference covers the documented range and the resolution coupling across ten vendors in one table, so you are not hunting through ten separate doc pages. If you are specifically planning a long single Kling shot, our 15-second Kling 3.0 planning guide covers the per-shot arithmetic that a flat duration number doesn't show you.

Aspect ratio follows the same pattern of a short list that varies by vendor rather than one universal set of options. Google's own Gemini API documentation for Veo restricts every current Veo variant to 16:9 and 9:16, nothing else. Kling's own API reference adds a native 1:1 option on top of those two. Neither vendor publishes a wider set than that, so if your deliverable needs a ratio outside those, budget time to crop after generation rather than assuming the model will produce it natively.

How do you get dialogue, sound, and music right?

Audio moved from an afterthought to a first-class prompt input once Veo and Kling both shipped native generation, and the two vendors default in opposite directions: Veo always generates audio unless you mute it after the fact, Kling stays silent unless you explicitly turn it on. Get that one default backwards and you either ship a silent Veo clip nobody meant to mute, or wonder why a Kling render has no sound at all. Our Veo dialogue prompting guide and Veo audio prompts guide cover Veo's side in full; AI video lip sync and dialogue compares what every model actually documents for speech; and our AI video sound design guide and music direction in video prompts cover sound effects, ambience, and score separately from dialogue.

How do you keep a character (and a shot) consistent?

The hardest failure mode in AI video isn't a single bad clip, it's a character whose face, outfit, or voice drifts across a sequence of them. Our video character consistency guide covers the reference-image and Element-binding techniques that hold identity across clips, and video transition prompts covers the adjacent problem of moving between two clips without the cut looking accidental. If your starting point is a still image rather than a blank prompt, image to video handoff prompts covers what changes when you're animating an existing photo instead of generating from scratch, and our AI video loop prompts guide covers the specific case of a clip that needs to repeat seamlessly.

Why does your AI video look fake, static, or warped?

Three distinct failure modes get reported as one vague complaint, "the video looks off," and each has its own fix rather than a shared one. If nothing in the frame seems to move, our AI video no motion guide covers how under-specified action defaults to near-stillness. If motion happens but bodies or objects distort as they move, why does my AI video morph and warp covers the temporal-consistency causes. And if the whole clip technically works but reads as obviously synthetic, why AI video looks fake covers the physics, lighting-continuity, and micro-motion tells that give it away, and how to prompt around each one.

Where to start

If you take one link from this page, make it the one that matches what you're stuck on right now, not the one that sounds most complete. Pick your model from the table above, borrow the prompt skeleton if you're starting from nothing, and use the troubleshooting section the moment something looks wrong rather than assuming a better model would have fixed it, since the same failure modes recur across all of them.

A reasonable order, if you genuinely have none of this yet: read the anatomy section, pick a model from the table based on whether you need dialogue, a longer single clip, or a lower price, write one prompt using the skeleton above translated into that model's own syntax, and only then go looking at camera vocabulary, sound design, or consistency techniques once a plain version of your idea is actually rendering. Layering technique onto a prompt that doesn't work yet just makes it harder to tell which change fixed anything.

Prompt Architects doesn't render any of these models' video itself; it generates and refines the prompt text you send to whichever one you pick, on the Advanced and Team plans. This page exists so choosing where to start doesn't take longer than writing the actual prompt.