How the Eight-Segment Prompt Skeleton Saves Massive Effort Across a Thousand Shots Without Cross-Contamination

If you wanted to manually write Jimeng video prompts for each of the 1,029 shots using traditional methods, the most conservative estimate is that each shot's prompt would require 150 to 300 characters, and you would have to ensure consistent style, uniform terminology, appropriate lip-sync and tone, and proper shooting techniques. This means writing roughly 150,000 to 300,000 characters of prompts for all 1,029 shots. But in the Yuncao Academy system, the process.aiwords field does not store a unique long-form text for each shot. Instead, it stores a complete prompt formed by filling three variable slots into a fixed skeleton of eight segments. The eight fixed segments are: character information, lip-sync consistency, accent and tone, shooting technique, scene location, on-screen action, aspect ratio, and resolution and duration. Five of the eight segments are completely fixed, and only three positions—scene location, on-screen action, and special requirements—are dynamically replaced based on the actual content of the storyboard. Therefore, the production team only needs to fill in three variables for each of the 1,029 shots. That workload is 3,087 variable values, which is two orders of magnitude less than hand-writing 300,000 characters of prompts. Take a typical long shot from the Five Elements Dragon Flag chapter as an example. The storyboard number is SCN000452, and it describes Lin Xuan standing before the East Gate, gazing at the distant army. The complete prompt generated in process.aiwords reads: Character Lin Xuan, appearance features: approximately 1.8 meters tall, black hair tied in a high ponytail, wearing an indigo narrow-sleeved long robe, a black iron long blade at his waist, defined facial contours, calm and resolute eyes. Lip-sync consistency requirement: this shot has no dialogue, lips closed naturally, note that the range of expression changes should not be excessive. Accent and tone requirement: no dialogue, no dubbing needed. Shooting technique: fixed camera position, slight low angle, medium depth of field, 35mm focal length, slow push-in. Scene location: East Gate, blue-gray city bricks, a stone-carved plaque above the gate arch, stone slab road on the ground, stone lions and flagpoles on both sides. On-screen action: Lin Xuan stands with hands behind his back, chin slightly raised, gaze passing over the gate toward the distant horizon, a gust of wind blowing through his robes. Aspect ratio 16:9, resolution 2560x1440, duration 5 seconds. This entire text is generated from a template, and only the action sentence is extracted from the storyboard's action field. When the same structure is applied to another character or another location, only the three variables need to be replaced. Liu Ruyan stands in the long-street market, also a fixed shot. The system replaces the character name from Lin Xuan to Liu Ruyan, the scene location from East Gate to long-street market, and the on-screen action from gazing to swinging a sword. That is all. The remaining five segments—character information, lip-sync consistency, accent and tone, shooting technique, and aspect ratio with resolution and duration—are all taken directly from the template. This replacement happens at the database level, not at the file-editing level. The developer only needs to fill in the corresponding codes in the rolex field, placecode field, and action field of the process table. When the storyboard is submitted to the Jimeng video generation task, the system assembles the complete prompt in real time. So modifying a shot only requires updating three cells in one row, without touching the eight segments of text at all. Meanwhile, the fixed nature of the template also brings a uniformity dividend. Since every prompt follows the same skeleton, the visual language of the shots becomes highly consistent. In the actual finished film "The First Avenue," if these two fixed shots were edited together, the audience would not feel any abruptness, because the shooting-technique segment is always the standard one-third composition and fixed camera position, the lip-sync consistency and resolution settings are also uniform, and only the scene and action change. Imagine writing prompts manually in the traditional way, with two different editors each writing one of these two shots. There is a high probability that one would set a zoom push-in and the other an orbit shot, and someone might even add arbitrary lighting or color adjectives, causing a conflict in shot style. Template-based writing eliminates this kind of human variation, essentially providing a unified director of photography preset for every shot. The statistics across the entire project are even more compelling. The book has 69 chapters and a total of 1,029 storyboards, each generating one process.aiwords record. The fixed skeleton portions of these records total approximately 2.3 million characters, but 98 percent of that is repetitive. The true effective variation is only 3,017 variable values, with an average length of about 21 Chinese characters per variable, totaling 63,360 Chinese characters. In other words, one person using one machine could process all variables in about two hours, and the extraction of these variables was already completed automatically during the funx.atctoscene deconstruction, with no manual intervention required. If done by hand, assuming each skilled prompt engineer can complete 50 high-quality prompts per day, completing 1,029 shots would take at least 24 working days, with no guarantee of consistency. So the efficiency gain from templating approaches twenty-fold, and this does not even account for the incremental cost of later revisions. During the production of Five Elements Dragon Flag, one revision perfectly demonstrated the advantage of this structured approach. In the original storyboard, Liu Ruyan's sword-swing action was a wrist twist with a diagonal slash downward. After generation, it was deemed too fierce, and the director wanted it changed to a horizontal slash. A traditional workflow would require rewriting the entire prompt. Here, it just meant rewriting the action field of that storyboard row in the process table to "horizontal slash," resetting the task status to pending, and resubmitting to Jimeng. This revision went from decision to video regeneration in less than four minutes. In a manual-prompt mode, it would take at least an hour to analyze the scope of the change and rewrite the relevant sections. Another example occurred during final review. Viewers reported that Lin Xuan's clothing color in the Five Elements Dragon Flag scene where he stands before the city gate did not transition naturally with the previous scene. In traditional production, you would need to go back to the image generation stage and regenerate the character image. In Yuncao Academy, because the image generation prompts and video prompts share the same template, modifying the cloth_main_color field in dbvideo_roles for Lin Xuan to ink blue, then resetting that shot's arcpictask and video tasks, the system automatically updates and regenerates not only the image but also the character description part of the Jimeng video prompt, which is dynamically pulled from the character table and therefore also becomes ink blue. This linkage effect is only possible with template-based technology, not with plain-text prompts. Error avoidance is another major benefit of templating. When an inexperienced creator writes prompts by hand, it is very easy to forget to specify the aspect ratio and cause incorrect frame proportions, or to forget the bitrate requirement and cause the post-production output to mismatch the finished film specs. In the fixed eight-segment skeleton, aspect ratio, resolution, and duration are hard-coded, with no variable position left for the user to fill in, making it impossible to write them incorrectly due to human oversight. The default rules in the lip-sync consistency and accent and tone segments also prevent novices from missing lip-sync alignment requirements when writing dialogue scenes. In actual testing, five new authors using the Yuncao Academy for the first time each attempted to manually write prompts for ten storyboards without any training. The average error rate was about 40 percent, mainly consisting of omitting the on-screen action or describing the shooting technique too casually. After using the template, as long as they correctly filled in the scene and action, the output quality immediately reached the level of senior team members. The template not only improves efficiency but also lowers the entry barrier, allowing people who do not understand prompt writing to achieve professional-quality results. The final conclusion is clear: engineering the prompt is more durable and reliable than piling on human resources, because the template itself is a distillation of optimally tuned best practices, and the three variable slots ensure that each shot's uniqueness is not erased by the template. A thousand people, a thousand shots, no cross-contamination.