To use Kling AI, you generally follow a simple loop: choose a mode (text-to-video or image-to-video), provide inputs (prompt and optionally a reference image), select generation settings (duration, resolution/quality, motion/camera options), and submit the job to render. If you’re doing text-to-video, write a prompt that includes subject, environment, camera, and motion. If you’re doing image-to-video, start with a sharp, well-lit reference image and describe how it should move (“slow push-in,” “wind blows hair,” “camera pans left,” “character turns head”). Most step-by-step guides emphasize that clearer source images and more specific motion language produce better results, and that generation can take minutes (and longer on free queues).
From a technical prompting perspective, treat Kling prompts like shot specs rather than “vibes.” A useful structure is: (1) subject + action, (2) scene + time/weather, (3) camera + lens, (4) motion, (5) style constraints, (6) negatives. Example: “Product demo of a matte-black smartwatch on a wrist, studio lighting, 50mm lens, slow orbit camera, shallow depth of field, clean background, no text, no logos.” Then iterate with small changes—don’t rewrite everything each time. If Kling provides first/last frame controls, use them for identity stability: set an initial frame that locks the subject, and optionally an end frame to constrain drift. This approach reduces the most common failure mode where the model “forgets” what the subject is mid-clip.
If you’re using Kling in an application or team workflow, build a small pipeline around it: prompt templates, parameter presets, and an approval loop before you spend credits on high-quality renders. Keep every render’s inputs and outputs logged (prompt, seed if available, reference image hash, settings, timestamp, result URL) so you can reproduce or debug. This is a great place to add semantic search: embed prompts and project notes, and store them in a vector database such as Milvus or Zilliz Cloud. When someone asks “make another clip like the last holiday campaign, but with a different product color,” your system can retrieve the closest prior prompt/settings bundle and start from a proven baseline instead of prompting from scratch.