How-toVideo ProductionAI

Turn an Article into a Vertical Video Script with AI: A Step-by-Step Tutorial

ccorrect

2026-01-29

10 min read

Practical prompts, templates, and a checklist to convert articles into 15–60s vertical microdramas using AI tools like Higgsfield and Holywater-era platforms.

Turn an Article into a Vertical Video Script with AI: Fast, Repeatable Steps for 2026

Hook: If you’re a creator, editor, or publisher losing hours to manual scriptwriting and inconsistent short-form videos, this guide gives you plug-and-play prompts, structure templates, and a production checklist to convert any article into a bingeable vertical microdrama or explainer using AI video tools in 2026.

Why this matters now (the TL;DR)

Mobile-first, short episodic vertical video is mainstream in 2026. Startups like Higgsfield and platforms backed by major studios (for example, Holywater’s 2026 expansion) have made AI video generation and vertical streaming tightly integrated with content discovery. That means audiences expect serialized, native-vertical experiences — and publishers who can turn articles into short videos fast win reach, retention, and new IP.

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

What you’ll get from this tutorial

Clear, repeatable process to convert an article into a 15–60s vertical script or a short episodic microdrama
Concrete LLM prompts and AI video tool prompts (works with Higgsfield-style editors and similar tools)
Structure templates for single-shots, microdramas, and 3-episode arcs
A production checklist that covers assets, editing, captions, and distribution

Step 1 — Quick triage: What kind of video should this be?

Before you prompt any AI, decide one of three formats. This choice defines pace, visuals, and dialogue.

Explainer (15–30s): One core insight, fast hook, single CTA.
Mini-story / Microdrama (30–60s): A character, conflict, and twist — high retention if serialized.
Episodic Sequence (3–5 episodes, 30–45s each): Break the article into beats for weekly vertical drops — perfect for building IP and subscription platforms like Holywater.

Step 2 — Extract the narrative beats from the article (3-minute method)

Open the article and pull these elements into a short doc. This is the input for your AI prompts.

Headline and 1-sentence thesis.
Three key facts or conflicts (bulleted).
Quotes or standout lines (one or two max).
Desired CTA (subscribe, read long form, visit product, watch episode 2).

Example: For a piece about “AI microdramas for mobile,” you might extract: thesis, rising consumer demand, investor signals (Higgsfield valuation), a founder quote, and CTA: “Watch episode 2.”

Step 3 — Use these LLM prompts to generate vertical scripts

Below are tested prompts that convert your extracted beats into formatted scripts for 15s, 30s, 60s and episodic microdrama. Replace bracketed items with your article content.

Prompt A — 15-second explainer script

Prompt to paste into an LLM (concise):

Convert this article summary into a 15-second vertical video script. Use three beats: Hook (1 line), Value (2 lines), CTA (1 line). Use snappy spoken lines and add on-screen caption notes in ALL CAPS wrapped in brackets. Keep tone: urgent, friendly. Source: [paste 1-sentence thesis and 3 bullets].

Output example:

HOOK: "Phones changed storytelling — now stories fit your thumb." [ON-SCREEN: HOOK]
VALUE: "Start with a single conflict. Build 2-3 scenes. Release weekly." [ON-SCREEN: TIPS]
CTA: "Want a script from your article? Tap to watch episode 2." [ON-SCREEN: CTA BUTTON]

Prompt B — 30–60s microdrama script (single episode)

Prompt for LLM:

Turn this article extract into a 45-second vertical microdrama script. Create a main character, a conflict tied to the article's key insight, one turning point at 30 seconds, and a cliffhanger CTA. Provide: scene headers (Scene 1 / Scene 2), on-screen captions, suggested shot types (close-up, mid, insert), and one-line music direction.

Output structure (example):

Scene 1 (0–15s): CLOSE-UP of character scrolling; text overlay: "When your feed decides your story"; line: "I keep losing my thread."
Scene 2 (15–35s): MID shot; conflict escalates; reveal a tool or idea from the article; line: "But what if episodes could be made from any article?"
Scene 3 (35–45s): INSERT / CLOSE-UP; twist/cliffhanger; CTA overlay: "Episode 2: How to make it — tomorrow."

Prompt C — Breakdown into a 3-episode arc

Prompt for LLM:

Using this article extract, create three 35-second episode scripts for vertical release. Episode 1: Hook + problem. Episode 2: Deepen conflict and show one technique. Episode 3: Reveal outcome and CTA to longform. Provide shot list, suggested captions, and exact lines (<=12 words each) for voiceover.

Step 4 — Concrete prompts for AI video tools (Higgsfield-style and others)

After you have a script, you’ll instruct a video-generation platform. Use these templates — they work with tools that accept stage directions and scene metadata.

Scene upload / creation prompt

Create a vertical 9:16 video, 45 seconds. Scene 1: Indoor coffee shop, early morning, close-up, actor female 20–30, expression: frustrated. Dialogue: "I keep losing my thread." Caption: "When your feed decides your story." Music: sparse piano, low tempo. Color grade: warm, high contrast.

Visual style prompt for AI generator

Style: mobile-native microdrama — short cuts, rhythm on 2/4, jump cuts at 2–4 seconds, high contrast faces, shallow depth of field. Include natural sound stings for scene changes. Add dynamic captions synced exactly with speech (words highlighted as spoken).

Prompt to a tool like Higgsfield for editing and variations

Produce three variations: A (emotional), B (informative with insets), C (fast cuts for Reels/TikTok). Export each at 9:16 and generate 15s edit for ads. Keep captions on. Also provide an audio-descriptive transcript SRT and a 2-line thumbnail text.

Step 5 — Shot list and asset checklist (what to prepare)

Whether using full AI generation or hybrid (real footage + AI), prepare these assets:

High-res logo and style kit (fonts, primary color hex, 2 caption styles)
Article highlights doc (headline, 3 bullets, 2 quotes)
Character references or permissive stock clips
Music track choices (instrumental stems if possible)
Thumbnail image or frame timecode for auto-snap

Step 6 — Fast editing workflow with AI tools

Use an LLM to produce the script and on-screen captions.
Feed the script into your AI video tool (see Higgsfield-style prompts above).
Generate a rough cut; export an H.264 9:16 draft.
Run a caption pass and a brand overlay pass via the tool.
Generate 2–3 style variations to A/B test thumbnails and opening 3 seconds.

Step 7 — Optimization for platforms and algorithms (2026 trends)

In 2026 the algorithmic emphasis is on early retention and repeat watch signals. Optimize like this:

First 3 seconds: Must contain the hook, an image, and on-screen caption.
Captions: Always on — use staggered kinetic text to increase view time.
Cliffhanger or loop: End with a reveal or visual loop to encourage rewatches.
Thumbnails: Use face + bold verb; platforms prefer thumbnails with human faces in 2026 tests.

Microdrama script template (copy/paste ready)

Use this template to standardize episodes.

Scene 1 (0–12s): [HOOK] Visual: CLOSE-UP. Line: "[HOOK LINE]". Caption: [HOOK CAPTION]
Scene 2 (12–28s): [RISING ACTION] Visual: MID/POV. Line: "[SHIFT LINE]". Caption: [KEY FACT]
Scene 3 (28–35s): [CLIMAX] Visual: INSERT/CLOSE-UP. Line: "[TWIST]". Caption: [CTA]
End Frame (35–45s): CTA overlay + subscribe card + episode tease

Sample filled microdrama (from an article on AI verticals)

Quick example so you see it end-to-end.

Scene 1: CLOSE-UP. "They say binge-watching is dying — but vertical? It's exploding." [ON-SCREEN: WHY VERTICAL MATTERS]
Scene 2: MID. "Studios and startups are building for series under a minute." [ON-SCREEN: HIGGSFIELD, HOLYWATER]
Scene 3: CLOSE-UP. "So we made Episode 2 from a single article — and it hooked 40% more viewers." [ON-SCREEN: WATCH EPISODE 2]

Production checklist — pre to publish (print this)

Confirm format: 15s / 30s / 45s / multi-episode plan.
Extract article beats and finalize voice + CTA.
Run LLM script prompts and choose the best script variant.
Prepare assets: brand kit, music, stock clips, actor references.
Input scene prompts into AI video tool (generate 2–3 drafts). Use tools like Higgsfield if you have access.
Do caption pass and color grade pass; lock assets.
Export required aspect ratios (9:16 primary, 1:1 or 16:9 if repurposing).
Upload with metadata: title (hook + keyword), 1-line description, tags, episode number.
Set release schedule and A/B test thumbnail + first 3 seconds.
Measure: retention at 3s, 6s, 15s; click-through to article; follower lift.

Measurement & iteration — metrics to track (2026 priorities)

Track these KPIs per episode and per series:

Early retention: Drop at 0–3s, 3–10s — critical for discovery. See the Analytics Playbook for how to instrument and report them.
Rewatch rate: Indicator of loopability and virality.
CTAs completed: Clicks to longform, subscriptions, or next episode.
IP signals: Does the article-to-series produce predictable lift for topics? Tag and measure by topic to feed editorial planning.

Advanced strategies: scaling and brand voice consistency

When you scale, two problems appear: inconsistent voice and workflow friction. Solve them with:

Voice guide prompt: Create a 50-word style prompt for your LLM and pin it: tone, vocabulary, sentence length. Prepend this prompt to every conversion task.
Template library: Store the microdrama template, episode templates, and caption styles in your CMS for rapid reuse. Consider monetization and packaging ideas from the micro-subscriptions playbook when you scale.
Data loop: Feed platform retention metrics back into your AI prompts to nudge scripts toward higher retention patterns.

Legal and ethical checklist (must-do in 2026)

Confirm rights for any actor likenesses, music, and stock footage.
Label AI-generated content where required by platform or region—see legal guidance on cloud caching and privacy for related compliance considerations: Legal & Privacy Implications for Cloud Caching in 2026.
Don’t misattribute quotes — use the article's source text and fact-check any generated claims.

Case example: From article to episodic pilot (a 90-minute editorial sprint)

Scenario: Your newsroom wants a pilot series from a data-driven article about vertical streaming.

Hour 0–1: Extract beats + decide 3-episode arc.
Hour 1–2: LLM generates 3 episode scripts; team selects one variant per episode.
Hour 2–3: Feed scripts into AI video tool; generate three rough cuts.
Hour 3–4: Team reviews, composes captions, selects music. Two quick revisions.
Hour 4–5: Export and schedule releases; create teaser for social amplification and consider a small live component or watch party to kick off the pilot (Live Q&A monetization ideas apply here).
Post-launch: Measure retention, iterate on scripts for Episode 4 based on data.

Final tips — common pitfalls and fixes

Problem: Script is too wordy. Fix: Enforce "<=12 words per spoken line" rule in your prompt.
Problem: No visual variety. Fix: Add specific shot types in the scene prompt (OV, CU, POV, INSERT).
Problem: Low early retention. Fix: A/B test different opening hooks and thumbnail faces.
Problem: Brand voice drifts. Fix: Prepend your canonical voice guide to every prompt.

Why this workflow beats traditional repurposing in 2026

Because AI tools like Higgsfield and platforms building vertical-first distribution (see Holywater’s 2026 expansion) let teams move from article to a publish-ready vertical asset in hours, not days. The most successful teams pair LLM-driven script generation with targeted scene prompts and a strong data feedback loop to tune for retention and monetization. That’s how you scale quality without exploding headcount.

Actionable takeaways — what to do next (right now)

Pick an article and extract headline + 3 bullets (5 minutes).
Run Prompt A and Prompt B in your LLM to get a 15s and 45s script (10 minutes).
Feed the 45s script into your AI video tool with the scene prompts above and generate a draft (30–90 minutes depending on tool).
Publish a test episode and measure 3s and 15s retention — iterate next episode using the data (1 week cadence).

Call to action

Ready to turn your backlog of articles into serialized vertical video that performs? Download the printable production checklist and three ready-to-use prompt templates to run your first pilot in under a day. Or start a free trial with an AI video tool that accepts scene prompts (look for Higgsfield-style editing workflows) and run the 5-step sprint above.

Start now: pick one article, run the 3-minute extraction, and use Prompt B to create your first microdrama script — then test a 15s cut. If you want the checklist and templates in a zip, click to request them from our editorial toolkit.

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