From your first prompt to finished film.

Text models share the same grammar but reward different habits. Claude thrives on rich context and explicit reasoning. ChatGPT responds to system messages and step-by-step framing. Gemini favors concise, well-scoped tasks. We compare identical prompts across all three.

• → Lesson: System vs. user messages
• → Lesson: Chain-of-thought and self-critique
• → Exercise: Same task, three models

Compose an image prompt like a shot list: subject, environment, lens and focal length, lighting direction, color palette, mood, and rendering style. Learn parameters like aspect ratio, stylize, and seed, plus how negative prompts and image references sharpen the result.

• → Lesson: The seven components of an image prompt
• → Lesson: Parameters, seeds, and negatives
• → Exercise: Match a reference photo

Consistency comes from anchors: a detailed character description you reuse verbatim, a reference sheet the model can see, character-reference parameters, and locked seeds. We build a repeatable identity token that survives new scenes, outfits, and angles.

• → Lesson: Building a reusable character description
• → Lesson: Reference images and identity anchors
• → Exercise: One hero, five scenes

Video prompts add motion to composition: camera moves, subject action, pacing, and duration. Learn to describe a dolly-in, a whip pan, or a slow push, and how to keep a character consistent across cuts by chaining reference frames between clips.

• → Lesson: Camera and motion vocabulary
• → Lesson: Maintaining characters across clips
• → Exercise: A three-shot sequence

Real projects chain tools. Use a text model to write a shot list, an image model to lock the look, and a video model to bring it to life. We build an end-to-end workflow and cover how to keep prompts, seeds, and references organized across a whole production.

• → Lesson: Text → image → video pipelines
• → Lesson: Versioning your prompts
• → Exercise: Ship a 15-second scene