Why do some AI models give vague or nonsensical answers, while others respond like a focused subject-matter expert? The difference is often not the size of the model, but the quality of the data used to fine-tune it.
At the center of that difference is SFT (Supervised Fine-Tuning) data. In this article, I will use a recent project Hansem Global delivered for a global tech company to show how high-precision SFT data turns a general-purpose model into a reliable “expert.”
SFT Data: From Smart Graduate to Job-Ready Specialist
You can think of a pre-trained model as a smart new college graduate. It has read a huge amount of information and has broad knowledge, but it does not yet know how your business actually works.
SFT is the process of turning that graduate into a job-ready employee for your specific domain. Instead of just feeding more random data, you teach the model:
“When you see this type of request (prompt), you must answer in this way, under these constraints, and avoid these mistakes.”
Hansem Global’s SFT data services focus on designing and producing exactly this kind of high-quality, rule-driven training data: structured prompts, explicit constraints, and carefully controlled examples that teach the model how to behave in real production environments.
Case Study: “Draw a Real Person, Not Six Fingers”
Recently, a global tech company asked Hansem Global to build SFT data for its image generation model.
The goal was not simply “make beautiful pictures.” The client wanted to eliminate the typical weaknesses of image models:
- distorted or impossible human bodies
- awkward lighting and artificial “AI look”
- cultural inaccuracies and stereotypes
To do that, they defined extremely detailed guidelines. Based on those guidelines, we built tens of thousands of high-precision prompt examples that the model could learn from.
Below are three core dimensions of that work.
1. Technical Quality: “It Should Not Look Like AI”
First, we had to remove the technical artifacts that make images look fake. The prompts were engineered to push the model toward realistic, DSLR-like output and away from common AI glitches. For example:
- Preventing body distortions
We explicitly included conditions such as “no distorted limbs, no incorrect number of fingers” to address a well-known weakness of generative models: hands and body structures. The prompts not only described the scene, but also instructed the model what must not happen. - Enforcing natural textures
We emphasized instructions like “natural skin and hair textures” and discouraged visual effects such as extreme over-sharpening or neon-like glow. The goal was subtle, believable realism rather than “AI demo” aesthetics.
These constraints were not added randomly. They were systematically embedded into the prompt patterns so that every training example reinforced good habits and blocked bad ones.
2. Cultural Authenticity: “That Building Does Not Exist There”
For a global service, cultural accuracy is not a “nice to have.” It is essential. A scene that looks plausible in one country may feel completely wrong in another if architecture, clothing, or everyday objects are off.
Our SFT data therefore embedded cultural authenticity at the prompt level:
- No invented or mixed cultural elements
Prompts explicitly avoided imaginary blends of cultures. For example, if the scene was set in a European café, the architectural features, furniture, and street details had to match that region. The same level of care applied to East Asian urban streets, West African courtyards, and more. - Intentional diversity
We made sure the data would not over-represent only one age group or demographic. Prompts included elders, children, and families, as well as people with pets and other everyday life situations. This helped the model learn a more balanced and inclusive view of human life.
In other words, we were not only describing “a person in a city.” We were encoding local context so that users in each market would feel, “This looks like my world.”
3. Structured Prompting: A Five-Part Formula
To reduce ambiguity for the model, every prompt followed a fixed, five-part structure. This made the data internally consistent and easier for the model to learn from:
- Main subject – Who or what is the focus?
- Action / emotion – What are they doing or feeling?
- Setting – Where is this happening?
- Cultural details – What local or contextual elements matter?
- Quality instructions – What technical and stylistic constraints must be followed?
For example (simplified):
- Main subject: “middle-aged woman reading a book”
- Action / emotion: “relaxed, gentle smile”
- Setting: “small independent café in a European city, afternoon light”
- Cultural details: “local architecture, wooden chairs, handwritten menu in the local language”
- Quality instructions: “natural skin texture, correct hands and fingers, soft realistic lighting, no neon effects, no distorted body parts”
Multiply this pattern across tens of thousands of scenarios and you get an SFT dataset where every example teaches both content and constraints in a consistent way.
Why Hansem Global? The “Density” of the Data
The real work in this project was not just “writing a lot of text.” It was translating complex human requirements into a form that a model can reliably understand and internalize.
That is where Hansem Global differentiates itself: the density of useful information in each data point.
Our approach to SFT data design includes:
- Fact-anchored data to reduce hallucinations
We build prompts and responses that are grounded in verifiable facts and clear rules, especially important when the model is used in high-risk or information-sensitive domains. - Multilingual and multicultural context
As a company with long experience in technical documentation and localization, we design data that respects local languages, cultural norms, and usage scenarios—rather than assuming one culture fits all. - Precision prompt engineering to work around model limits
We do not treat the model as perfect. We study its failure patterns and design prompts and constraints that compensate for those weaknesses, as in the “no distorted hands, realistic lighting” example above.
In short, we do more than label data. We architect the learning experience for the model.
If Your AI Feels “2% Off,” Look at the Data, Not Just the Model
Many teams feel that their AI system is “almost good enough,” but still misses the mark in subtle ways: slightly wrong tone, small factual slips, or visuals that do not feel real. The instinct is often to switch to a larger model.
Our experience suggests a different first step: examine the quality and structure of your SFT data.
If you want your AI to behave like a real expert—aligned with your brand, your workflows, and your markets—model size alone will not get you there. You need training data that captures your domain logic, your cultural context, and your quality standards with high precision.
That is exactly the space where Hansem Global operates: designing and producing high-density SFT datasets that turn general-purpose models into domain specialists. If your AI feels 2% short of where it should be, it may be time to upgrade your data, not just your model.