AI Video Generation

AI video generation uses artificial intelligence to create video clips from text prompts or still images. Tools like Higgsfield, Cling, and Seedance take a prompt (a text description) or a starting image and turn it into a short video scene. The quality of that raw clip is never perfect — it will always need post-production (editing work you do after generation), like splicing scenes together and engineering a story.

This matters for AI development because it teaches you to think in workflows. You don't just hit generate; you build a pipeline. For example, Claude Code can write the video prompts for you, then feed those into a video generator. Open-source frameworks like Remotion and HyperFrames let an AI agent (an autonomous program that performs tasks) write the code for an entire video in under a minute — even if you don't know how to code. That changes how developers think about scaling content. A human editor manually adjusting keyframes can't compete with an agent that writes and edits video code instantly.

Right now is the worst AI video generation will ever be — it improves every month. The skill that matters most is clear communication: describing exactly what you need in a prompt, understanding process, and where the holes in that process are. Companies are already making this shift, and demand for people who can build these systems is growing. Learning this now puts you ahead.

To create AI videos, start by generating your starting images. Use a tool like Nano Banana (an AI image and video generator) to produce the visuals you need. For example, you might have Nano Banana create two different images and then turn them into a video. You can access Nano Banana through platforms like Key.ai, which offers fast performance.

Once your images are ready, use an AI video generator to animate them. Tools like Higgsfield compile many image and video generation models into one interface. For better results, you can use Claude (an AI assistant) or Claude Code to help write video prompts. Claude Code works similarly to Claude but focuses on coding tasks. You feed the prompts into the video generator, then take the output back to Claude Code to build a website or other project around the video.

Be prepared that AI videos won't come out perfect. Post-production editing is important. Splice scenes together in editing software like DaVinci Resolve or Adobe Premiere Pro to match your vision. For each scene, generate individual clips by copying and pasting your starting image, then chatting with the AI until you get the exact motion you want. This step-by-step process keeps quality high while scaling your output.

AI video generation is powerful but has clear pitfalls. First, never expect a perfect output on the first try. AI videos "are never going to come out perfect," so post-production (editing after generation) is essential. Splice your clips into separate scenes to match your vision. The key is to "reiterate and do it over and over again" until the result works.

A major mistake is ignoring consistency across scenes. Without careful prompting, you lose character clothing, environment, or product appearance. Use a structured scene-by-scene framework to organize details like setup and clothing. This prevents the "mess" of mismatched visuals.

Best practices start with using Claude Code to create refined video prompts rather than one-shotting (sending a single prompt) into tools like Nano Banana alone. Claude can "work with us on the entire process," making generation more repeatable and scalable. For example, you generate a prompt, feed it into a video generator, then return to Claude Code to build a website around the video.

Combine tools intentionally. Use Nano Banana for effects like glowing elements, then drop footage into DaVinci Resolve or Premiere Pro for final polish. Not every step needs AI — mixing traditional editing with generation gives better results.

Finally, remember that "this is the worst that AI video generation models will ever be." Quality improves monthly, so treat current limits as temporary. Focus on iterative refinement, consistency planning, and hybrid workflows to get usable results today.