Introduction
Large Language Models (LLMs) are changing the way businesses generate content, connect with customers, and automate communication. But here’s the thing: an LLM is only as good as the data you train it on. High-quality training data can give you a competitive edge, while poor data can drag your model’s output (and your brand) down.
To get this right, you need a clear data preparation framework. We’ve broken this down into four pillars: data unification, bias mitigation, data annotation, and synthetic content generation.
We covered data unification in Part 1 and marketing bias in Part 2. In this third part, let’s dig into one of the most overlooked—but essential—steps: data annotation.
TL;DR
Data annotation adds metadata to training data to help models recognize nuances.
Marketing and IT teams jointly create an annotation matrix to capture the various annotations.
The annotation matrix serves as the foundation for building annotation data pipelines.
Why Data Annotation Matters in Marketing
When you fine-tune an LLM for marketing, you’re not just teaching it how to write—you’re teaching it why certain messages work and when to use them. Raw content alone won’t cut it. Without annotation, the model picks up superficial patterns but misses the deeper strategy behind successful messaging.
Here’s what goes wrong when you skip proper annotation:
- It doesn’t understand what separates good copy from generic filler
- It misses audience nuance and campaign goals
- Emotional appeal, CTA strength, and brand voice get inconsistent
- Your content may sound okay, but it won’t convert or connect
Annotations help the model internalize structure, tone, purpose, and value. You’re not just feeding it examples—you’re explaining what makes them work.
Real Example: A Beauty Brand’s Struggles and Breakthrough
A global beauty company wanted to fine-tune an LLM to write product descriptions across a massive catalog. Their dataset included 5,000+ items collected over the years, but the quality varied wildly.
Their first move? Hiring a generic annotation vendor.
The result?
- Surface-level quality assessment: Labeling a product description as “good” despite it failing to highlight unique selling points
- Inconsistent tone labeling: Marking casual phrases like “super amazing results” as formal tone in one item, while labeling similar content like “incredible transformation” as casual tone in another
- Inability to evaluate benefit articulation: Not recognizing vague claims like “improves skin” versus specific ones like “reduces fine lines by 27% in 14 days”
- Overlooked call-to-action effectiveness: Ignoring weak CTAs like “try it” versus compelling ones like “Reveal younger-looking skin today”
- Missed brand tone adherence: Not flagging overly technical language for a brand with a warm, conversational identity
- Resulted in flat content: Producing technically correct but uninspiring copy like “This moisturizer hydrates skin. It contains ingredients. Use daily.”
- Models mimicking superficial patterns: Generating descriptions that follow structure but miss strategic persuasion techniques
The fine-tuned model was technically accurate, but flat, generic, and uninspiring.
What Turned It Around
The real shift happened when the marketing and data teams worked together to build a strategic annotation rubric. Instead of basic tags, they scored each content sample on specific dimensions like:
Dimension | Poor (1) | Acceptable (3) | Excellent (5) |
Benefit Clarity | Vague or generic | Clear but basic | Specific, relevant, and compelling |
Technical Accuracy | Incorrect or vague | Correct but not detailed | Precise, substantiated, brand-compliant |
Brand Voice | Off-brand or inconsistent | Somewhat aligned | Strongly on-brand tone and language |
Emotional Appeal | Flat or dry | Lightly emotive | Resonates deeply with target audience |
Call-to-Action | Missing or generic | Standard CTA | Action-oriented, persuasive, personalized |
Differentiation | Generic claims | Some uniqueness | Clear, distinctive competitive positioning |
This rubric became the foundation for training. Each content item was annotated by dimension. These annotations didn’t just label the content—they guided the model to understand and reproduce the strategy.
What the Training Data Looks Like
Once annotated, each content sample was formatted for model training. Here’s what that looks like using a structure compatible with OpenAI and other instruction-tuned models:
{
"messages": [
{
"role": "user",
"content": "Generate a product description for a skincare product with the following qualities:\n{\n \"product\": \"Night Renewal Serum\",\n \"benefit_clarity\": 5,\n \"technical_accuracy\": 5,\n \"brand_voice\": 5,\n \"emotional_appeal\": 5,\n \"call_to_action\": 5,\n \"differentiation\": 5\n}"
},
{
"role": "assistant",
"content": "Reduce fine lines by 27% in just 14 days with our dermatologist-approved Night Renewal Serum. Formulated with Retinol+ Complex and hyaluronic acid, this luxurious treatment works overnight to restore your skin’s natural radiance. Feel confident, refreshed, and ready—wake up to visibly smoother skin. Shop now and reveal your best skin yet."
}
]
}
This structure helps the model learn to generate marketing content that reflects the intended tone, clarity, and conversion strategy based on inputs.
How to Structure Annotated Data for Fine-Tuning (Model-Agnostic)
No matter which foundation model you’re using—OpenAI, Anthropic, Mistral, Cohere—the process is similar. Here’s how to turn your annotations into a training-ready dataset:
1- Create a Unified Schema
Structure each sample using a message-based schema:
{
"messages": [
{ "role": "user", "content": "<prompt with annotations>" },
{ "role": "assistant", "content": "<desired output>" }
]
}
2- Convert Annotations Into Prompts
Use either structured prompts or natural-language style prompts.
Structured prompts (ideal for LLMs that parse JSON-like inputs). Example:
{
"product": "Night Renewal Serum",
"benefit_clarity": 5,
"technical_accuracy": 5,
"brand_voice": 5,
"emotional_appeal": 4,
"call_to_action": 5,
"differentiation": 5
}
Natural language prompts (more readable, ideal for chat-based models). Example:
Generate a product description for "Night Renewal Serum" with the following qualities:
- Benefit Clarity: 5
- Technical Accuracy: 5
- Brand Voice: 5
- Emotional Appeal: 4
- Call to Action: 5
- Differentiation: 5
3-Build Message Pairs
Construct a Python script (or similar workflow) to convert each annotated row into a JSON object with the messages structure:
{
"messages": [
{
"role": "user",
"content": “<prompt from step 2 above>”
},
{
"role": "assistant",
"content": "<final marketing text>"
}
]
}
Repeat this per content item and write each JSON object as a line in a .jsonl file. This .jsonl file then becomes your final training input.
4- Use for Fine-Tuning
Once structured, this dataset:
- Will work as-is with OpenAI
- Can be easily adapted for Anthropic, Mistral, LLaMA, Cohere, or any instruction-tuned model
- Replace “role” with “input”/”output” or flatten messages into a single prompt string if needed
Final Thoughts
Annotation isn’t just a data prep task—it’s how you teach your LLM to think like a marketer. When you add structure and strategy to your content samples, you give the model the context it needs to consistently generate high-quality, on-brand, conversion-friendly content.
Done right, annotation becomes your leverage: a way to scale your best marketing instincts across every channel, product, and audience.
Ready to take the next step?
If you’re thinking about fine-tuning a language model and want to explore how data annotation could work for your specific content and goals, we’d love to help. Schedule a FREE discovery call with our team to discuss your dataset, challenges, and use case—no strings attached.
Also, don’t forget to check out our other AI in marketing services.
