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For a more coherent AI-generated film, plan it shot by shot: keep a short description of the character and visual style, give each shot one main action, and specify its framing, camera movement, setting, lighting, and style. When the model supports it, use a reference image or reusable character input to anchor appearance, then use text to describe what moves. These methods can improve control, but they cannot guarantee perfect continuity.
Start with a continuity sheet
Before writing individual prompts, record the traits that should remain stable across the film. Keep the list concise so you can reuse it without burying each shot in description.
- Character identity: a few distinctive facial or silhouette details, hair, wardrobe, and accessories.
- Visual language: the film’s palette, lighting quality, and recurring environment details.
- Shot-specific changes: what the character does, where they move, and what the camera sees in that moment.
This is a practical planning method, not a prescribed template. OpenAI’s Sora 2 prompting guide discusses reusable character references, while Google DeepMind’s Veo prompt guide recommends describing subjects and visual details clearly.
Build each prompt around one shot
Give each generation one principal action and a clear camera idea. A useful draft pattern is: “A [framing] shot of [character] [single action] in [environment]. [Camera movement]. [Lighting and style].” Treat it as a starting point rather than a rigid formula: Runway says its text-to-video guidance does not require a strict structure, though a consistent structure can help with iteration.
For example: “Wide establishing shot of a red-haired courier in a mustard raincoat crossing a quiet station platform at dawn. The camera slowly tracks beside her as a train passes in the background. Cool mist, warm practical lights, restrained naturalistic film style.” This is an illustrative template, not a tested recipe.
Runway’s Gen-4 guide says its generations are 5- or 10-second clips and that it can help to consider each generation as a single scene. It also cautions that too many scene changes and instructions can lead to unintended results. Keep that duration guidance specific to Gen-4 rather than treating it as a limit for every video model. See the Gen-4 Video Prompting Guide.
Choose text-to-video or a visual reference
Text-to-video is useful when you are exploring a shot or do not have a defined starting image. If a specific character design or composition must carry into a shot, use a suitable image or a model’s reference feature when available. The image can establish appearance and composition; the prompt can then focus on motion rather than restating every visible detail.
Runway’s image-to-video guide advises treating the image as the visual starting point and describing the desired motion in the prompt. OpenAI’s Sora 2 guide likewise describes image input as an anchor for the first frame, with text directing what happens next. Neither approach guarantees that every later frame will preserve all details exactly.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsUse continuity features only as documented for the model
Reference workflows differ by product and version. Check the current documentation for the model you are using, and name that model and version in any instructions you share with collaborators.
- Google Veo 3.1: Google’s Gemini API documentation says Veo 3.1 accepts up to three reference images to guide content and preserve a person’s, character’s, or product’s appearance. This is a documented Veo 3.1 feature, not a general guarantee across video generators. See Generate videos with Veo 3.1 in Gemini API.
- OpenAI Sora 2: The prompting guide describes image input as a first-frame anchor and reusable character references created from a short reference clip. Availability and behavior may depend on the current product and access conditions; consult the Sora 2 Prompting Guide.
These are different implementations, not interchangeable controls. Product documentation describes available features and suggested workflows; it does not establish which platform produces the most consistent results in a controlled comparison.
Describe action, framing, and camera movement precisely
A shot prompt should tell the model both what changes and how the viewer sees it. Include only details that matter to the shot:
- Framing: wide shot, medium shot, close-up, or another clear view.
- Action: one main movement, with a direction or pace when useful.
- Camera: static, tracking, pan, tilt, dolly-in, or another specific move.
- Environment: the location and any background activity relevant to the shot.
- Lighting and style: qualities such as soft overcast light, warm practical lights, or a restrained naturalistic look.
- Timing or direction: include these when they clarify the intended motion.
Runway’s Text to Video Prompting Guide offers the structure “ [Camera] shot of [a subject/object] [action] in [environment]” as an optional way to organize a prompt. Google DeepMind’s Veo guide also covers prompt details such as subject, action, camera, and style. Avoid piling on several camera moves or unrelated actions if the shot is meant to feel continuous.
Refine one important variable at a time
- Generate a simple first version. Use the continuity description or reference, one action, and one camera idea.
- Identify the mismatch. Decide whether the action, camera, or visual design is the part that needs work.
- Change only that part. If the action works but the camera does not, keep the image, action, and style fixed while changing the camera instruction—for example, from “static eye-level medium shot” to “slow low-angle dolly-in.”
- Compare the result and repeat. If the design drifts, strengthen the reference or concise stable description rather than adding unrelated instructions.
Runway recommends adding one prompt element at a time in its Gen-4 workflow. This makes it easier to understand what a revision changed, though outcomes still depend on the model and its inputs. The example above illustrates an iteration method; it is not a validated recipe.
What consistency prompting can—and cannot—do
Clear shot planning, references, and controlled iteration give a video model better-defined inputs. They are not evidence of perfect character continuity, and the product guidance cited here does not establish a measured improvement rate or a head-to-head ranking of platforms. Choose a workflow based on its documented inputs—text, first-frame image, multiple references, or reusable character input—and the degree of shot control you need.
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