I Built an AI Text-to-Video Generator — Here's What I Learned About Prompt Engineering for Video

My journey into creating CineGen, an AI text-to-video generator, taught me that prompt engineering for video goes far beyond image prompting. Here are the key lessons I discovered:

First, a video prompt is a timeline, not a static image. An image prompt describes a single moment, while a video prompt sequences multiple moments. A static scene prompt often leads to the model inventing unnatural motion. For example, "beautiful sunset over the ocean, cinematic" fails to provide the model with the necessary direction.

Instead, a more effective prompt like "Wide aerial shot of an ocean at sunset. The camera slowly pans left across the water as waves roll toward the shore. Orange light flickers on the wave crests. A small sailboat crosses the frame from right to left. Cinematic lighting, calm mood." addresses the key elements: what's in the frame, what's moving, and how the shot evolves.

I find it useful to adopt a three-beat arc: establish, action, resolve. This structure helps the model generate a more coherent video output.

Second, camera language plays a crucial role in generating high-quality video. Generative video models respond well to cinematography vocabulary, more so than you might expect. Using specific camera terms can significantly improve the output. For instance, instead of describing a scene, "Tracking shot following a hiker from behind on a forest trail, camera gliding smoothly at walking pace.

Tall pine trees blur past on both sides, morning fog drifting between trunks. Shallow depth of field, natural light." gives the model clear instructions on the camera movement. Remember, one primary camera move per clip works best. Avoid stacking contradictory camera moves, as this creates a motion puzzle that the model struggles to solve.

Third, verbs are more effective than adjectives in video prompts. In image prompting, adjectives like "beautiful" or "stunning" are crucial. However, in video prompting, adjectives often add noise. Instead, focus on specifying motion with verbs that include direction, speed, and rhythm. For example, "Waterfall plunging down a mossy cliff into a pool below, mist rising and drifting left.

Ferns swaying gently in the foreground. Camera holds a static wide shot." This prompt is stronger because it uses verbs to convey motion rather than relying on adjectives. Each noun in the prompt should have a corresponding verb attached to it, even if the action is simply standing still. Speed words like "slowly," "gently," "rapidly," or "suddenly" also play a significant role, as the model can differentiate these effectively.

Fourth, temporal consistency is the most critical aspect of AI video generation. Making sure that frames 1 and 48 agree with each other is far more challenging than producing pretty individual frames. To maintain consistency, anchor the subject with specific, repeated attributes. Instead of saying "a woman," specify "a woman with short black hair in a red jacket."

Color anchors also work well, as color tends to remain stable across frames. Keep actions simple and avoid complex, multi-stage actions within a single clip. Instead, break them down into separate clips and transition between them. Lastly, use negative prompts in video generation, as they are essential for preventing artifacts such as morphing faces, extra limbs, flickering, warping backgrounds, and text or watermark issues.

A negative prompt example: "negative prompt: morphing face, extra limbs, extra fingers, flickering, warping background, distorted hands, text, watermark, sudden scene change, deformed body, disappearing objects."