AI Video Generation & Avatars
Prompt engineering for video generation, avatar use cases, storyboarding, cross-shot consistency, and disclosure of AI-generated media.
Generated video is uneven in a way that demonstrations do not show, because a demonstration shows the shot that worked. These seven articles are about the unevenness: which shot types come out usable often enough to plan a production around, and which are lotteries. The first maps that, by shot type, and explains why the failures cluster where they do — hands, text, physical contact, and anything that has to stay the same between two shots. The second covers prompts, using a five-part structure that specifies the camera, the light and the action rather than adding adjectives about mood. The third and fourth are about the two problems that unplanned generation creates: no sequence, because each generation is independent, and no continuity, because nothing carries between them. Both articles are as much about restructuring to avoid the problem as about solving it. The fifth covers avatars — where they hold up, why cutting away regularly is the technique rather than a preference, and the updatability argument that is their strongest practical case. The sixth is about a format rather than a tool: how a whiteboard explainer is structured across five beats, and why stopping the drawing is the strongest emphasis available in it. The last is about disclosure, and it is written around a test rather than a rule, because platform requirements differ and change. Capability claims here are deliberately qualitative; the shape of what is hard has been more stable than any specific limit, and advice tied to a particular model's current abilities dates faster than it is useful.
All 7 articles in AI Video Generation & Avatars
Disclosing AI-Generated Media to Your Audience
Platform labels answer a compliance question. The one that decides whether an audience keeps trusting you is different: would they feel misled on finding out, and did you tell them before they found out.
Using AI Avatars for Talking-Head Content
An avatar is convincing for a sentence and increasingly odd across a minute. That single property decides where the format works and dictates the editing approach it needs.
Structuring a Whiteboard Explainer That Holds Attention
Whiteboard explainers work because the picture arrives one piece at a time, in step with the argument. They fail when the drawing becomes decoration running alongside a narration that has moved on.
Keeping Characters and Style Consistent Across Shots
Each generated shot is produced from scratch with no memory of the last one. Every technique for continuity is a way of smuggling information from one generation into the next, and all of them are partial.
Storyboarding Before You Generate Anything
Cheap shots invite generating first and deciding later, which produces a folder of clips that do not cut together. A shot list costs an hour and decides what never needs generating at all.
Writing Prompts That Produce Usable Video
Most weak prompts are weak in the same way: they describe a subject and leave the camera, the light and the movement unspecified, so the model chooses all three.
What AI Video Generation Can and Cannot Do Right Now
The useful question is not whether generated video looks good — sometimes it looks remarkable. It is which shot types come out usable often enough to plan a production around.