How to Use LLMs to Generate UGC Scripts (2025)

UGC ads generate 4x higher CTR than traditional brand ads—but only when they feel authentic.

The moment a script sounds rehearsed or uses marketing language, the performance advantage disappears.

This creates a challenge when using LLMs (Large Language Models) to generate scripts. By default, LLMs produce polished, grammatically correct, professional-sounding text. Exactly what you don't want for UGC.

The solution is learning to prompt LLMs for authenticity, not polish. When done correctly, LLMs can generate UGC script frameworks at scale that creators can deliver naturally.

Here's the practical framework.

Why LLMs Can (and Can't) Write UGC Scripts

What LLMs do well:

• Generate diverse messaging variations quickly
• Apply storytelling frameworks consistently
• Create structured talking points
• Adapt tone based on detailed instructions
• Produce volume for testing

What LLMs struggle with:

• Sounding unrehearsed without specific prompting
• Avoiding marketing buzzwords naturally
• Understanding platform-specific language norms
• Knowing which imperfections feel authentic vs sloppy

The solution:

Use LLMs to generate talking point frameworks, not verbatim scripts. Creators add their natural voice, verbal patterns, and authentic delivery.

The UGC Script Prompt Framework

Standard prompts produce marketing copy. UGC-specific prompts produce authentic frameworks.

Basic UGC script prompt structure:

Role: You are writing talking point guidelines for UGC creators filming mobile app testimonials.

Context:
- App: [name and value prop]
- Target audience: [demographics and pain points]
- Desired ad length: [15-20 seconds]
- Platform: [TikTok, Instagram, etc.]
- Winning patterns: [what has worked before]

Task: Generate [number] talking point frameworks for UGC creators. These should be guidelines, not verbatim scripts, allowing creators to use their own words.

Format for each script:
1. Hook (first 3 seconds) - suggest 2-3 options
2. Problem/context (5-7 seconds) - key points to cover
3. Discovery/solution (5-7 seconds) - app introduction approach
4. Outcome (3-5 seconds) - specific result to mention
5. CTA (2-3 seconds) - natural recommendation style

Authenticity requirements:
- First-person perspective only
- Conversational tone, use contractions
- Specific details over abstract benefits
- Include "actually," "honestly," or similar conversational markers
- Write like telling a friend, not like marketing copy
- Use simple language, avoid buzzwords like "revolutionary," "game-changing"
- Allow grammatical imperfections
- Include specific numbers, timeframes, or examples

Example output:

Concept 1: The Month-End Broke Pattern

Hook options:
- "I was always broke by like the 15th of every month"
- "Why am I always surprised by my bank balance?"
- "I used to have no idea where my money went"

Problem (talking points):
- Describe the feeling of checking your bank account and being confused/frustrated
- Mention making decent money but having nothing left
- Share specific moment that made you realize you needed to track better

Discovery (talking points):
- How you found or heard about [AppName]
- Initial skepticism (if true) - "I didn't think another app would help"
- What made you actually try it

Outcome (specific to mention):
- Exact amount saved (e.g., "$400 in two months")
- Specific insight discovered (e.g., "I was spending $200/month on subscriptions I forgot about")
- How it feels now

CTA (natural recommendation):
- "If you're dealing with the same thing, it's honestly worth trying"
- "I'll link it below if you want to check it out"

Platform-Specific Prompt Adjustments

TikTok Scripts

Add to prompt:

"TikTok-specific requirements:

• Hook must work in first 1-2 seconds
• Use TikTok language patterns (storytelling style, casual)
• Consider trending sounds or formats
• Allow for on-screen text to carry some message
• Slightly more dramatic delivery acceptable"

Instagram Reels/Stories

Add to prompt:

"Instagram-specific requirements:

• Can be slightly more polished than TikTok
• Captions should work without sound
• Fits more lifestyle-integrated content
• Visual aesthetic matters more"

Facebook Feed

Add to prompt:

"Facebook-specific requirements:

• Must work completely without sound (captions carry everything)
• Slightly older audience, adjust language
• Can be longer (20-30 seconds)
• More testimonial-style acceptable"

The Talking Points vs Full Script Distinction

Bad prompt (produces scripted-sounding output):

"Write a 20-second UGC script for a budget tracking app"

Better prompt:

"Create talking point guidelines for a UGC creator testimonial about a budget app. Format as bullet points of key messages, not verbatim dialogue. Creator should use their own words to express these ideas."

Why this matters:

When you ask for a "script," LLMs default to complete, polished sentences. When you ask for "talking points," they provide frameworks that creators naturally adapt.

Adding Authenticity Constraints

Include these specific instructions to prevent overly polished output:

Conversational markers:

"Include conversational hesitations like 'actually,' 'honestly,' 'like,' or 'you know' where natural. Use 1-2 per script maximum."

Contractions:

"Use contractions throughout (I've, it's, didn't) not formal grammar (I have, it is, did not)"

Specific over abstract:

"Replace any abstract benefit with specific examples:

• Instead of 'save money' → 'saved $400'
• Instead of 'fast' → 'takes 10 seconds'
• Instead of 'easy' → 'three taps and done'"

Grammatical imperfections:

"Allow sentence fragments. Start sentences with 'And' or 'But.' End with prepositions. Use incomplete thoughts where natural."

Real person language:

"Write as if the creator is telling a friend, not presenting in a meeting. Use phrases like 'so I tried this app' not 'I discovered a solution'"

Multi-Variation Generation Strategy

Generate volume, then select best fit.

Efficient workflow:

Prompt: "Generate 10 different talking point frameworks for [concept], varying the hook and specific proof points. Keep the problem-solution structure consistent."

Output: 10 variations with different hooks and examples

Filter: Select 3-4 that best fit your creative matrix and are production-feasible

Refine: "For concept #3, generate 5 variations that change only the hook while keeping the rest constant"

Final selection: 2-3 scripts to send to creators

This approach produces 30-50 options in minutes, from which you select the strongest.

The Human Review Process

Always review AI-generated scripts for these authenticity killers:

Red flags to fix:

✗ "Revolutionary," "game-changing," "transform your life"

✓ "Actually works," "makes it easier," "helped me"

✗ Perfect grammar throughout

✓ Conversational imperfections, fragments

✗ Third-person product descriptions

✓ First-person experience sharing

✗ Abstract benefits without specifics

✓ Concrete numbers, timeframes, examples

✗ Formal language structure

✓ Casual, as-if-telling-a-friend phrasing

Quality control checklist:

• First-person throughout?
• Conversational tone?
• Specific proof points (numbers, timeframes)?
• No marketing buzzwords?
• Natural CTA, not pushy?
• Could a real person say this naturally?

Iterating Based on Creator Feedback

After creators use AI-generated scripts, ask:

"Which talking points felt most natural to deliver? Which felt scripted or uncomfortable?"

Common feedback patterns:

"The hook felt forced" → Revise prompt to request more casual openings

"The numbers felt too specific" → Allow ranges or qualitative statements

"The CTA was awkward" → Generate softer recommendation styles

Feed this back into your prompts to improve future outputs.

Advanced Techniques

Teaching the LLM Your Voice

Prompt:

"Here are 3 UGC scripts that performed well for my app:

[Script 1] [Script 2] [Script 3]

Analyze the tone, structure, and language patterns. Then generate 5 new talking point frameworks that match this style but with different hooks and examples."

Concept-to-Script Pipeline

Step 1: Generate concepts using AI

Step 2: Select winning concept

Step 3: Generate 10 script variations for the concept

Step 4: Select 2-3 scripts

Step 5: Generate hook variations for each script

Step 6: Final selection for production

Platform Adaptation

Prompt:

"Take this talking point framework and adapt it for:

1. TikTok (faster, more dramatic)
2. Instagram Reels (slightly polished)
3. Facebook (works without sound)"

Produces platform-optimized versions from one concept.

Measuring AI Script Performance

Track which LLM-generated scripts perform vs human-written:

Script Source	Authenticity Score (1-5)	CTR	CPI
Human-written	4.2	2.8%	$2.50
LLM + human review	3.9	2.7%	$2.55
LLM unreviewed	2.8	2.1%	$3.20

The performance gap between reviewed and unreviewed AI scripts validates the human review step.

Workflow Efficiency

Time comparison (generating 5 UGC scripts):

Traditional human writing: 2-3 hours

LLM generation (no review): 5 minutes → poor authenticity

LLM + human review: 20-30 minutes → good authenticity

LLM + creator collaboration: 40 minutes → best authenticity

Optimal workflow:

1. Generate 20 talking point frameworks (5 minutes)
2. Filter to 5-8 promising (10 minutes)
3. Review and edit for authenticity (15 minutes)
4. Send to creators with choice of which to use
5. Creators deliver in their own words

Total time: 30 minutes to produce 5-8 high-quality UGC script options.

FAQs

Can LLMs write authentic-sounding UGC scripts?

Yes, when prompted correctly for talking points rather than verbatim scripts. LLMs excel at generating conversational frameworks that creators adapt in their own voice. The key is requesting 80-120 word talking point outlines with authenticity constraints, not full scripted dialogue.

What LLMs work best for UGC script generation?

ChatGPT (GPT-4), Claude (Sonnet), and Gemini all work well. ChatGPT tends to be most conversational, Claude follows complex instructions well, and Gemini can analyze existing UGC examples. Test each to see which produces the most natural-sounding output for your specific needs.

How do I prevent AI-generated scripts from sounding too polished?

Include specific instructions: use contractions, conversational language, first-person perspective, include verbal hesitations like 'actually' or 'honestly,' allow grammatical imperfections, and request 'how you'd tell a friend' phrasing. Always review output for marketing buzzwords and remove overly perfect grammar.

Should I give creators AI-generated scripts or talking points?

Give talking points, not verbatim scripts. Provide the framework, key messages, and specific details that must be included (app name, dollar amount saved, etc.), but let creators use their own words. This preserves authenticity while ensuring messaging consistency.

How many script variations should I generate per concept?

Generate 10-20 variations, filter to 5-8 based on strategic fit and authenticity, review/edit the top 5-8, then send 2-3 final options to each creator. This gives creators choice while maintaining quality control.

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LLMs can dramatically accelerate UGC script creation when used correctly—not as a replacement for human creativity, but as a tool for generating diverse frameworks that creators deliver in their authentic voice.