videoproductionprompts

Prompted Storyboarding: From LLM Outlines to Shot Lists for Vertical Video

aaiprompts

2026-02-14

10 min read

Turn LLM outlines into vertical shot lists and editor notes. Ready to use templates, JSON schemas, and API examples for 2026 workflows.

Hook: Stop guessing how to shoot from LLM outlines

Are you frustrated by inconsistent AI outputs when you ask an LLM for a scene outline, only to lose time converting that text into a usable shot lists for vertical video? You are not alone. Teams building microdrama, episodic shorts, or mobile-first ads need reliable, repeatable pipelines that convert text outlines into aspect-aware framing prompts, and actionable editor notes. This guide gives you a production-ready workflow in 2026, with templates, JSON schemas, API examples, and best practices for integrating with cloud tools and VFX systems.

Why this matters in 2026

Vertical-first platforms scaled dramatically through late 2025 and into 2026. Venture-backed companies focused on episodic vertical content are raising capital to treat vertical as a primary delivery format. This shift means production teams must move beyond adapting horizontal assets to designing for vertical from the start. LLMs can accelerate ideation, but without structured conversion they produce inconsistent handoffs between writers, directors, DPs, editors, and VFX artists.

Example trend: vertical streaming platforms scaled their content pipelines in 2025 and 2026, driving higher demand for precise shot descriptions and aspect-aware visual planning.

The inverted pyramid: What you need first

At the top level, every LLM to shot list pipeline should produce three artifacts in a single pass:

Structured shot list in machine readable format, with fields for timecode, shot type, framing, movement, and metadata.
Aspect-aware framing prompts that specify composition rules for vertical ratios like 9:16, 4:5, and 2:3.
Editor notes containing cut points, transitions, sound design cues, and VFX placeholders suitable for ingest into NLEs or cloud render queues.

High-level workflow

Prompt an LLM for a story outline or microdrama treatment.
Use a conversion prompt to transform the outline into a structured shot list.
Generate aspect-aware framing prompts for each shot.
Auto-create editor notes and JSON metadata for cloud workflows.
Validate and version the shot package, then push to the production API or asset management system.

Why automation beats manual conversion

Manual interpretation introduces variability. Automated conversion enforces consistent terminology, aspect rules, and metadata so downstream systems like VFX, editing APIs, or remote production assistants can act deterministically. This is essential for scale when producing serialized microdrama where episodic templates must be reused and iterated fast.

Step 1: Prompting the LLM for an outline

Start with a concise creative prompt that includes format constrains, runtime, and tone. Example prompt pattern:

Write a 90 second microdrama treatment for vertical video.
Include 4 beats, each 15-30 seconds long. Tone: tense, intimate.
List characters and one-line motivations.
Return concise scene descriptions only.

Key inputs to collect:

Runtime target (eg 60, 90, 180 seconds).
Episode format (microdrama, ad, product demo).
Primary deliverable aspect ratios (eg 9:16 for vertical, 4:5 for social).
Constraints like available locations, cast, or VFX budget.

Step 2: Convert outline into a structured shot list

Feed the LLM outline into a conversion prompt that outputs a bounded, machine-friendly structure. Encourage the LLM to follow a JSON like vocabulary, but to keep single quote formatting to avoid breaking simple parsers in example code. Your conversion prompt should enforce fields like id, start, duration, shot_type, framing, movement, hero_action, sound, vfx_placeholder. If you need inspiration for automating the conversion step, see work on AI summarization and agent workflows that automate structured output generation.

Shot list prompt template

Convert the treatment into a shot list. For each beat, create shots with fields:
- id
- start_seconds
- duration_seconds
- shot_type (closeup, medium, wide, insert)
- framing (subject placement, negative space) tuned for 9:16
- camera_movement (static, dolly_in, pan, handheld)
- hero_action (what actor does)
- dialogue_or_sound
- editor_note (cut, dissolve, smash_cut)
- vfx_tag (if any)
Return the results as an array of objects using single quotes.

Sample output concept

[
  { 'id': 'S1', 'start_seconds': 0, 'duration_seconds': 8, 'shot_type': 'closeup', 'framing': 'tight on eyes, top third, center vertical', 'camera_movement': 'steady', 'hero_action': 'breathes sharply', 'dialogue_or_sound': 'silence, heavy breath', 'editor_note': 'cut on breath', 'vfx_tag': null },
  { 'id': 'S2', 'start_seconds': 8, 'duration_seconds': 12, 'shot_type': 'medium', 'framing': 'two-shot stacked, subject on lower third', 'camera_movement': 'push_in', 'hero_action': 'stands, turns', 'dialogue_or_sound': 'ambient city', 'editor_note': 'match cut to hand', 'vfx_tag': 'screen overlay' }
]

Store this output as canonical shot metadata. That single source of truth powers storyboards, animatics, VFX briefs, and edit timelines. For storage and on-device considerations, consult guidance on on-device AI storage and metadata.

Step 3: Generate aspect-aware framing prompts

Vertical framing differs from horizontal in how negative space, headroom, and motion are composed. Create framing prompts that are explicit about the aspect ratio and composition rules. Include safe zones and mobile UI overlays.

Framing prompt pattern

For aspect 9:16 generate a concise framing directive for the shot id S1.
Include:
- subject placement using thirds and vertical anchors
- headroom limits in pixels percent
- safe zone for captions and UI (bottom 15 percent)
- motion path for keyframes if camera moves
Return a single line directive.

Example framing directive

S1 framing for 9:16: tight closeup. Subject eyes placed at 33 percent from top. Leave 18 percent bottom safe zone for captions. Top margin 8 percent. No lateral negative space. For push in, animate vertical center shift 5 percent over 2 seconds.

These directives are used in three production places:

Storyboard panels and shot diagrams for the DP.
Prompting image generation or previsualization models to create reference frames.
Embedding metadata for NLE import so editors preserve safe zones.

Step 4: Editor notes and cut-level instructions

Editor notes should be actionable and timestamped. A standard editor note contains cut timing, transition type, preferred L cuts or J cuts, and placeholder names for sound beds and VFX assets. Keep notes short and machine-readable for automatic ingest into editing APIs and cloud render queues.

Editor note example

S1 editor_note: cut at 8s. Transition none. Apply L cut on S2 dialogue starting 7.6s. Insert sound bed 'city_ambiance_v2' at master -10db. Flag VFX placeholder 'screen_overlay_01' starting 10s duration 6s.

Step 5: Packaging metadata for cloud workflows

Package shot lists, framing directives, and editor notes as JSON-like objects that your asset manager or editing API can ingest. Include references to production assets, permissions, and version tags. Use semantic fields so CI systems can validate and route tasks to VFX, editorial, or camera departments.

Minimal shot package schema

{
  'project_id': 'proj_2026_001',
  'episode': 'E01',
  'aspect_ratios': ['9:16','4:5'],
  'shots': [ ... shot list objects ... ],
  'version': 'v1.2',
  'approved': false,
  'metadata': { 'location': 'cafe_interior', 'cast': ['actor_a','actor_b'] }
}

Push this package to your production API. Example orchestration call in pseudocode:

client.post('/api/shotpackages', body=shot_package)

Integrations and APIs

To scale, wire the conversion step into automated triggers. Common integration points:

LLM endpoint or cloud function that returns structured shot lists
Asset management system for images, VFX plates, and sound cues
Editing platform APIs for timeline creation and JSON import
VFX task queues with tags from vfx_tag fields

Example pipeline sequence

Writer generates treatment in CMS
Webhook triggers conversion LLM which returns shot package
Shot package saved to storage and assigned tasks via workflow engine
Previs images generated by image model using framing directives
Editor imports shot package to create rough cut and timeline

Previsualization with image and video models

Use the framing directives to prompt image generation or short previs clips. When prompting vision models, include aspect ratio tokens, safe zones, and motion paths. Example prompt components for an image generator:

Aspect: 9:16
Style: filmic, 35mm equivalent, natural lighting
Subject: head-and-shoulders, eyes at 33 percent from top
Overlay: transparent bottom 18 percent safe zone marker

Mapping shot list fields to VFX briefs

For every shot with vfx_tag, generate a VFX brief that contains plate resolution, stabilization notes, tracking markers, and expected deliverables. Attach the vfx_tag to the shot package so the VFX system can create ticketed tasks automatically. If you need examples of how to ticket and route visual tasks at scale, see practices for evidence capture and task routing at edge networks.

Quality controls, governance, and versioning

In 2026, teams expect production-grade controls on AI outputs. Include these elements in your pipeline:

Schema validation on shot packages to catch missing fields before consumption. (Automate with your CI and API schema tools like those described in the integration blueprint.)
Prompt versioning so you can revert to earlier conversion templates — track versions alongside the model ID in your prompt audit.
Access controls on who can approve shot packages and release to editorial.
Audit trails for LLM prompts and model versions used for a given episode.

Testing and iteration

Run A/B tests on framing directives and edit styles. Use small batches of episodes to test whether a 5 percent vertical shift improves viewer retention in short form. Measure results with platform analytics and feed winners back into your prompt templates.

Advanced strategies for scale

Build a library of shot archetypes for microdrama: close intimacy, panic pullback, reveal wide, insert object. Use these to standardize prompts.
Parameterize prompts with tokens for actor height, mobility, and safe zones so the same template covers different casts and sets.
Add a lightweight preview generator that stitches AI-generated frames into animatics, reducing live shoot uncertainty. For fast capture and reference-camera workflows, check compact capture kits like the PocketCam Pro field reviews.
Use role-based LLM prompts: one prompt tuned for directors, another for editors, another for VFX leads.

Real-world example: from outline to shot in 6 steps

Writer creates a 120 second treatment for episode E02
System triggers conversion LLM which returns 18 shot objects
Framing directives generate 18 reference frames for 9:16 and 4:5
Previs images are produced and reviewed by the DP
Editor imports shot package and applies editor notes to timeline
VFX tasks are auto-created for three shots with vfx_tag

Template prompts and quick copy

Keep a prompt library with templates mapped to use cases. Example templates you should store and version:

Treatment to outline
Outline to shot list
Shot to framing directive per aspect
Shot to VFX brief
Shot to editor note

Security, privacy, and IP considerations

When using LLMs, record model IDs and timestamps. Be careful with sending sensitive scripts or unreleased IP to external APIs. For regulated productions, use private instances or encrypted prompts and ensure your storage respects rights and permissions. Track consent for actor imagery used in AI previews. If you're weighing which LLM to trust with sensitive materials, see comparisons like Gemini vs Claude discussions and run internal audits before sending raw media to external services.

Future predictions and closing thoughts for 2026

Expect deeper integration between LLMs, generative video models, and NLEs in 2026. Vertical-first streaming platforms and micro app style production tools will continue to drive demand for predictable, scalable shot conversion pipelines. Teams that standardize prompts, metadata schemas, and cloud APIs will reduce iteration cycles, lower costs, and increase output velocity.

Actionable takeaways

Always output a machine readable shot package from your LLM conversion step.
Generate explicit aspect-aware framing directives for each target ratio.
Embed editor notes and VFX tags in the same shot package to automate task routing.
Version prompts and validate schema to maintain production quality.
Measure viewer response to framing variants and iterate using A/B experiments.

Call to action

Ready to standardize your LLM to vertical production pipeline? Export your next treatment through the templates in this guide and run a pilot that auto-generates shot packages, framing directives, and editor notes into your asset manager. If you want a starter prompt library and JSON schemas to import into your workflow, download the companion toolkit or request a demo of our orchestration templates and API connectors.

aiprompts

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Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.