How to generate video from Kling AI?

To generate a video in Kling AI, you generally choose Text-to-Video or Image-to-Video, provide your inputs (a text prompt and optionally a reference image), pick output settings (duration, aspect ratio, quality), and submit the job. Text-to-Video is best when you’re describing a scene from scratch (“a close-up shot of a coffee cup steaming on a wooden table, morning light, slow push-in”). Image-to-Video is best when you need the subject to match a specific look (product photo, character design, brand asset) and you want to animate it (“slow orbit around the product,” “subtle wind motion,” “camera pans left”). After generation, you review the clip, iterate by adjusting the prompt or settings, and export the result (often with different rules for watermarks depending on your plan).

For developer-grade results, treat prompting like writing a shot specification, not a vibe. A reliable prompt structure is:

1. Subject + action (what is moving),
2. Environment (where/when),
3. Camera (lens/shot type),
4. Motion (camera movement and subject movement),
5. Style constraints (photoreal vs illustrative, lighting),
6. Negatives (what must not appear). Example: “Matte-black smartwatch on wrist, studio lighting, 50mm lens close-up, slow orbit camera, shallow depth of field, clean background, no text, no logos, no extra fingers.” Start simple, then add constraints only when needed. If Kling supports negative prompts, use them for common failure modes (unwanted text overlays, warped hands, extra limbs, background clutter). Also adopt a two-phase pipeline: generate a short/cheap preview to validate composition and motion, then run the high-quality render only after the prompt stabilizes. This single change usually cuts your retry costs more than any “prompt trick.”

If you’re generating videos as part of a team or product workflow, build a thin layer of orchestration around Kling: job templates, parameter presets, and logging. Save every run’s metadata (prompt, negative prompt, seed if available, duration, model/version, reference image hash, output URL) so you can reproduce or debug outcomes. This is also where semantic reuse pays off: most teams generate the same “types” of shots repeatedly (product hero loop, b-roll, character turn). Store your best prompts and settings as reusable assets, then retrieve them when a new request resembles an older one. A vector database such as Milvus or Zilliz Cloud can make that retrieval automatic: embed prior prompt bundles and tag them with metadata like “camera orbit,” “studio product,” “night street,” “anime style,” and “no text,” then fetch the closest match as a starting point. That turns “video generation” into a repeatable engineering workflow instead of trial-and-error.