Video workflows with ChatGPT, Suno, and Tesseract: A phased prompt guide

LinkTutorial

Mirage outlines a practical workflow using ChatGPT as an orchestrating agent to combine Suno music generation, image storyboarding, and layered video composition in Tesseract.

Key points

  1. Five-step process: selecting music in Suno, mapping waveform timings, generating scene storyboards in ChatGPT, building layered sequences in Tesseract, and iterative frame review.
  2. Audio-first timing: visual cues, text reveals, and transitions are explicitly mapped to audio beats, subdivisions, and energy changes before composition begins.
  3. Storyboard visual targets: generating high-fidelity still frames first gives the agent explicit visual benchmarks to reconstruct inside the video editor.
  4. Non-destructive editing: Tesseract maintains editable text, timing, media assets, and effect layers within the same project instead of baking flat video outputs.
  5. Adobe interchange: finished edits can be exported to Adobe Premiere Pro (.prproj) or experimentally to After Effects (.aep) for post-production.

Why it matters

While standard generative video models produce immutable video clips, this approach combines LLM orchestration with multi-track, layered project files. It bridges the gap between generative concepting and professional editing requirements.

Analysis

The guide demonstrates a disciplined workflow for generative media: separating soundtrack creation, frame design, and animation logic. Providing the AI agent with a precise waveform timing map and reference storyboard images minimizes visual drift and hallucinations. For motion designers and creative teams, maintaining an editable project file allows granular revisions to specific layers without re-rendering the entire sequence from scratch.

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Summary generated by AI (Gemini 3.8 Flash, 5 November 2026). It may contain errors – the original source is authoritative.

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