Text-to-video generation — tools like Runway, Pika, and Luma Dream Machine turning a written prompt directly into a short moving clip — has matured fast, but the workflow that actually produces something usable looks different from how most people approach it on their first try.
Watch: How to Make an AI Video with Runway AI Beginner Tutorial 2026 (Merle Becker, YouTube)
Step 1: Think in shots, not scenes
Every current text-to-video tool works best over short clips — typically a handful of seconds — and coherence degrades the longer and more complex a single prompt tries to be. Rather than describing an entire scene’s worth of action in one prompt, describe one shot: a single camera position, a single clear action. Multi-shot sequences get assembled afterward by generating several short clips and editing them together — treating the tool as a shot generator, not a scene generator, is the single biggest mental shift that separates usable output from a muddled first attempt.
Step 2: Image-to-video beats text-to-video for control
Most platforms support image-to-video generation — uploading a still image (a photo, or a frame from an AI image generator) and having the tool animate it — alongside pure text-to-video. Starting from an image gives dramatically more control over composition, character appearance, and framing than describing it purely in text, since the tool only has to solve “how does this move” rather than “what does this even look like.” For anything where a specific visual result matters, generate the still first (in whatever image tool gives the best control), then animate it.
Step 3: Camera language in your prompt does real work
Text-to-video models respond meaningfully to actual cinematography vocabulary — “slow dolly in,” “static wide shot,” “handheld tracking shot” — rather than only describing subject matter. Including explicit camera movement and shot-type language in a prompt produces more predictable, more filmic results than a prompt that only describes what’s in frame, since it gives the model an explicit instruction for the one variable (camera behavior) that’s otherwise being guessed at.
Step 4: Expect to re-roll, and generate in batches
Individual generations are inconsistent — the same prompt can produce a great result once and a broken one the next time. Generating multiple variations of the same prompt in one batch, then picking the best result, is standard practice rather than a sign the prompt needs rewriting. Budget for this in both time and any per-generation credits a platform charges — the realistic first-attempt success rate for a genuinely usable clip is well under 100%, even with a well-constructed prompt.