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Workflow Templates for Creating Micro‑drama Episodes with LLMs and Video AI

aaiprompts

2026-02-05

10 min read

End-to-end templates to turn a seed idea into 9:16 microdrama episodes: prompts, script JSON, shot lists, Video AI orchestration, and postproduction automation.

Hook: Your team wastes hours on ad-hoc prompts and slow edits — here’s a replicable pipeline that produces vertical microdramas fast

Teams building mobile-first episodic content in 2026 face the same friction: inconsistent LLM outputs, no shared prompt library, and manual video stitching that kills velocity. If you want repeatable, brand-safe microdramas at scale, you need an end-to-end workflow that converts a single idea into a finished 9:16 episode through composable prompts, structured outputs, and cloud automation.

Why this matters in 2026

Vertical streaming and microdramas are no longer fringe. In January 2026, industry coverage highlighted how companies are raising capital to scale mobile-first episodic platforms — the trend is clear: short, serialized stories optimized for phones are winning attention (and ad dollars). As one report put it:

“Holywater is positioning itself as 'the Netflix' of vertical streaming.” — Forbes, Jan 2026

That matters because the technical bar is rising: producers expect rapid ideation, predictable scripts, automated shot lists, and API-driven postproduction.

What this article gives you

Below are practical, copy-pasteable workflow templates for producing short vertical episodes (15–60s). You’ll get:

IDEATION prompts and LLM call examples
SCRIPT templates for microdrama beats and constraints
SHOT LIST generation schema and JSON examples
Postproduction automation recipes (FFmpeg + Video AI APIs)
Cloud workflow patterns, governance and scaling tips

Quick overview: the 6-stage pipeline

Ideation — produce hookable concepts and loglines
Script generation — short, beat-driven scripts with time budgets
Shot list & storyboard JSON — machine-readable shot specs
Asset generation — call Video AI and TTS APIs for clips and voice
Postproduction automation — assembly, subtitles, grade, music
Publish & iterate — platform upload, analytics, feedback into prompts

1) Ideation: structured prompt templates

Stop asking LLMs for “ideas.” Give them constraints tuned for short vertical storytelling: character, conflict, hook, and a 15–60s runtime. Use a prompt that returns a ranked list and metadata for search.

Seed Prompt (copy/paste)

Goal: Produce 6 microdrama loglines optimized for vertical platforms; include tone, dominant visual, and 1-sentence thumbnail text.

Prompt: "You are a TV writer crafting microdrama vertical episodes (15–60s). Given the seed: {seed_text}, return 6 loglines ranked by virality potential. For each logline include: title (6 words max), logline (1 sentence), tone (1 word), dominant visual (1 phrase), thumbnail_text (6 words max), and a one-sentence reason this will hook mobile viewers. Output JSON array."

Example output (shortened):

[
  {
    "title":"Swipe Left, Burned",
    "logline":"A first date's text reveals a secret that changes everything.",
    "tone":"tense",
    "dominant_visual":"close-up on phone screen",
    "thumbnail_text":"He lied in 3 texts",
    "reason":"Relatable tech-based conflict + immediate reveal"
  }, ...]

Implementation tip: Store these JSON objects in a vector DB (Weaviate, Pinecone) with embeddings so your creative team can search by tone, hook, or dominant visual.

2) Script generation: micro-episodes with strict time budgets

Microdramas need sharp beats and precise timing. Use a prompt that outputs a timestamped script and stage directions optimized for 9:16 framing.

Script Template Prompt

Prompt: "Given the logline: {logline} and target_duration_seconds: {seconds}, generate a beat-level script in JSON. Each beat should include: start_time (s), end_time (s), action (1 sentence), dialogue (optional), camera_direction (one phrase), sound_design (one phrase). Keep total duration within ±1s. Use vertical framing guidance (e.g., 'tight close-up', 'over-the-shoulder vertical')."

Sample JSON beat:

{
  "beats":[
    {"start":0, "end":3, "action":"Phone buzzes on table; protagonist picks it up.", "dialogue":"", "camera":"tight close-up on phone", "sound":"notification chime"},
    {"start":3, "end":10, "action":"Protagonist reads text and freezes.", "dialogue":"(whisper) No...", "camera":"extreme close-up on eyes", "sound":"low pulse"}
  ]
  }

Why JSON? A structured script becomes a contract: your shot list JSON, video API, and editor all read the same spec.

3) Shot list generation: machine-readable and actionable

Convert beats into a shot list JSON with frame data, durations, motion, and required assets (actor, props, B-roll). The shot list is the input to Video AI renderers or editors.

Shot List Schema (example)

{
  "shots":[
    {
      "id":"S1",
      "start":0,
      "end":3,
      "type":"close_up",
      "camera_move":"static",
      "description":"Phone on table; notification visible",
      "assets":["phone_screen.png"],
      "voiceover":null
    },
    {
      "id":"S2",
      "start":3,
      "end":10,
      "type":"extreme_close_up",
      "camera_move":"subtle_dolly_in",
      "description":"Protagonist reacts to text",
      "assets":[],
      "voiceover":"(whisper) No..."
    }
  ]
  }

Use an LLM prompt to convert script JSON into this shot list schema automatically. That keeps human editors focused on creative checks instead of tedious mapping.

4) Asset generation & Video AI orchestration

In 2026 the majority of production teams rely on a mix of synthetic footage, stock, and live-action inserts. The shot list drives which API to call: a human actor plate vs. a synthetic scene from a Video AI provider. Design your orchestration layer to accept shot JSON and route it based on rules.

Routing rules (examples)

If shot.type == "close_up" and asset exists -> use stock plate + AI crop for 9:16
If shot requires a character and budget == low -> generate synthetic actor with Video AI + identity template
If dialogue present -> generate TTS voice clone or use actor voice, then match lip-sync via API

// pseudocode
  const llm = await fetchLLM('/generate_script', { logline });
  const shotList = await fetchLLM('/generate_shots', { script: llm.json });
  for (const shot of shotList.shots) {
    const provider = chooseProvider(shot);
    const clip = await provider.createClip(shot);
    await uploadClip(clip, storage);
  }
  await stitchClips(storage, finalOutput);

Integration tips: keep per-shot metadata (source_provider, cost_estimate, render_time). Use asynchronous workers (Cloud Run, AWS Fargate) to parallelize clip generation.

5) Automated postproduction

Once clips exist, automate assembly: stitch, subtitle, color grade, apply motion graphics (lower-thirds and brand bumpers), and export multiple aspect ratios. Here’s a minimal, reliable set of steps.

Stitch clips in order using FFmpeg concat or a Video API edit endpoint.
Generate captions from LLM or ASR; format as SRT/TTML and burn-in or provide closed captions.
Apply LUTs or machine-learned color grade profiles per brand tag.
Mix music and normalize loudness (–23 LUFS for broadcast; platform-specific targets may vary).
Export presets: 9:16 1080x1920 H.264 for TikTok/Instagram; 1080x1350 for IG feed; WebM variant for FAST platforms.

FFmpeg stitch + subtitle example

# create input list file
  printf "file '%s'\n" clip1.mp4 clip2.mp4 > inputs.txt
  # concat
  ffmpeg -f concat -safe 0 -i inputs.txt -c copy temp.mp4
  # burn subtitles and convert
  ffmpeg -i temp.mp4 -i captions.srt -c:v libx264 -vf "subtitles=captions.srt,scale=1080:1920:force_original_aspect_ratio=decrease" -c:a aac -b:a 128k final_9_16.mp4

Automate this in a cloud function triggered when all clips are uploaded. Use signed URLs and ephemeral credentials for security.

6) Publish, analytics, and feedback loop

Automated publishing is a two-way street: post to platforms via API and ingest viewership signals back into your prompt library. Use these signals to re-rank ideation outputs and refine scripts.

Publish metadata: title, hashtags, thumbnail_text from ideation step, chapters (if supported)
Collect: watch-through rate, replays, comments sentiment
Feed back into: prompt weighting, A/B script variants, and shot templates

Cloud workflow patterns & templates

Two scalable patterns work well:

1. Event-driven, serverless workers

Trigger: new project in DB -> Tasks: LLM script generation -> enqueue shot jobs to worker pool -> generate clips in parallel -> postproduction job -> publish. Use Pub/Sub / SQS and autoscaling containers.

2. Orchestrated Pipeline (YAML) — GitHub Actions or Cloud Workflows

name: microdrama-pipeline
  on:
    workflow_dispatch:
      inputs:
        logline:
          required: true
  jobs:
    generate-script:
      runs-on: ubuntu-latest
      steps:
        - name: call-llm-script
          run: node scripts/generate_script.js "$INPUT_LOGLINE"
    generate-shots:
      needs: generate-script
      runs-on: ubuntu-latest
      steps:
        - name: call-llm-shots
          run: node scripts/generate_shots.js
    render-clips:
      needs: generate-shots
      runs-on: ubuntu-latest
      steps:
        - name: dispatch-render-jobs
          run: node scripts/dispatch_renders.js
    postproduction:
      needs: render-clips
      runs-on: ubuntu-latest
      steps:
        - name: finalize
          run: node scripts/finalize_and_publish.js

Store prompt templates and version them in a repo. Use semantic commit messages like: prompt: tighten dialog constraints for 20s episodes.

Prompt library architecture

A robust library has:

Versioned templates in Git
Metadata (intended duration, tone, targeted platform)
Embeddings for semantic search
Access controls and audit logs

Example metadata JSON:

{
    "id":"script_template_v2",
    "duration_profile":"15-30",
    "platforms":["tiktok","instagram"],
    "last_updated":"2026-01-05",
    "owner":"creative_lead@studio.com"
  }

Governance, security & cost controls

As you scale, attention to governance pays off.

Secrets management: use cloud secret stores (Secret Manager, AWS Secrets Manager). Never check keys into Git.
Prompt review: require human approval for scripts flagged by safety models (copyright, PII, defamation). See our quick prompt review cheat sheet.
Cost controls: set quotas per project, per provider, and run dry-runs for expensive Video AI renders.
Audit & provenance: store rendered assets with metadata linking back to the shot JSON and prompt version.

Scaling patterns and performance tips

Parallelize per-shot renders to exploit modern Video AI GPU farms.
Cache reusable assets (background plates, SFX) to reduce repeated generation.
Use cheaper proxies for initial edits; replace with high-quality renders in final stages.
Instrument latency and cost per minute so creative decisions can be optimized against real dollar impact.

Real-world signals & predictions for creators in 2026

2025–2026 saw startups and studios doubling down on AI-driven vertical content: capital flows into platforms that can produce serialized verticals algorithmically. Expect three things this year:

Composability: LLMs and Video AI will be consumed as building blocks in workflows, not monolithic tools.
Data-driven IP: Platforms will discover high-potential loglines via analytics loops (Holywater-style productization of micro IP).
Tool democratization: Non-developers will continue building micro-apps and short-format series using low-code orchestration and prompt templates.

Example: end-to-end minimal pipeline (practical checklist)

Use this checklist to run your first automated microdrama pipeline in a week.

Pick a logline seed and run the Ideation prompt; choose one result.
Generate script JSON (target 20s) using the Script Template prompt.
Convert script to shot list JSON and decide which shots will be synthetic vs. live.
Dispatch rendering jobs to a Video AI provider (parallelized). Store clips in cloud storage.
Run FFmpeg assembly + subtitle burn to produce final 9:16 MP4.
Upload via platform API; tag metadata and track viewership signals.
Iterate: re-run ideation prompt but include analytics as input to bias toward higher-performing hooks.

Advanced strategies for teams and studios

A/B content generation: Produce two script variants per logline, render both, and test small-scale splits to measure watch-through and rewatches.
IP-first pipelines: generate themed batches (10–20 episodes) and use cross-episode motifs and music for serialized retention.
Localization: swap TTS and cultural cues by locale automatically using a localization prompt layer.
Human-in-the-loop hooks: add manual checkpoints for content with sensitive topics or brand constraints.

Final checklist: governance and readiness

Prompt library in Git with versioning and owner tags
Vector embeddings for prompt search and reuse
Secure API keys and per-provider quotas
Cost monitoring for per-minute rendering and LLM tokens
Analytics loop that feeds performance back into ideation

Conclusion & actionable next steps

Vertical microdramas are a high-impact content format for 2026. The competitive edge goes to teams that treat prompt engineering, shot specification, and postproduction as a single, versioned workflow. Use the templates above to standardize output, reduce iteration cycles, and scale creative experiments without blowing your render budget.

Immediate actions:

Fork a prompt template repo and add metadata for your brand’s tone.
Stand up a cloud function to auto-run the Script -> Shotlist -> Render pipeline for one 20s pilot.
Instrument analytics on watch-through and feed the top-performing hooks back into the ideation prompt.

Call to action

Ready to turn ideas into serialized verticals? Clone our starter pipeline (includes prompts, shot-list schema, and GitHub Actions examples) and run your first automated microdrama in under an hour. If you want the starter repo link or a tailored workshop for your team, reach out — we’ll help you map this pipeline into your stack and OSS tools.

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.