Part 10: Model-Specific Synthetic Data Tuning

Here is Part 10 of the Generative AI in Data Science blog series. This post goes model-specific. Now that you know how to generate, benchmark, and tune synthetic data, it’s time to align that data with the specific needs of different model architectures.

Synthetic data is not one-size-fits-all. A prompt that works great for a LightGBM classifier will fail miserably when fine-tuning an LLM.
And image-text pairs that look nice to humans won’t move the needle unless they are contrastively useful.

In this final post of the series, we will break down how to tune synthetic data for specific model types:

• LLM fine-tuning: Instruction-following with synthetic prompts + completions

• Vision models: Contrastive learning with image-text pairs

•  Tabular models: Boosting edge detection in tree-based systems

Let’s get into it.

1. LLM Fine-Tuning: Instruction-Following Synthetic Data

Large language models like GPT-4, Claude, or Mistral respond best to instructional data structured as:

{
  "prompt": "What are the health risks of a sedentary lifestyle?",
  "response": "Prolonged inactivity can lead to obesity, heart disease, diabetes..."
}

 Synthetic Strategy

Use GPT-4 to generate instruction → completion pairs:

instruction_prompt = """
Generate 100 realistic instruction-response pairs in the health domain.
Each should include:
- An instruction (question, command, or request)
- A helpful and informative response
Structure: JSON with "prompt" and "response"
"""

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": instruction_prompt}]
)

pairs = json.loads(response["choices"][0]["message"]["content"])

 Tune for Specific Behaviors:

• Add prompt types: open-ended, yes/no, multi-step reasoning

• Include user tone: polite, demanding, casual

• Add mistakes for robustness: e.g., misspelled instructions

 Example Use:

Fine-tune open-source LLMs (e.g., LLaMA, Mistral) with:

• 100k synthetic instruction-following pairs

• 20% from edge cases or model error patterns (feedback loop from Part 8)

 2. Vision Models: Contrastive Learning with Image-Text Pairs

Contrastive vision-language models (like CLIP, BLIP, Flamingo) learn by aligning:

• Positive pairs: matching image + text

• Negative pairs: mismatched samples

Synthetic Data Strategy

Step 1: Generate image metadata (text prompt + structured data)

products = [
  {
    "name": "Aurora Smart Mug",
    "features": ["Auto temperature control", "USB-C", "White ceramic"],
    "caption": "A smart ceramic mug with temperature control and a sleek white finish."
  },
  ...
]

Step 2: Use DALL·E to generate product images from the prompt

prompt = "Product photo of a white ceramic smart mug with a black lid, on a plain white background."
# Generate with DALL·E API

Step 3: Create contrastive pairs

positive = (image_embedding, text_embedding)  # aligned
negative = (image_embedding, unrelated_text_embedding)  # contrast

 Evaluate:

Use cosine similarity between embeddings. Good synthetic image-text pairs should cluster tightly in embedding space.

from sklearn.metrics.pairwise import cosine_similarity
similarity_score = cosine_similarity([img_embed], [text_embed])[0][0]

Train a CLIP-style model to distinguish true vs false pairs → use synthetic data to:

• Expand underrepresented classes

• Pre-train on synthetic before fine-tuning on real pairs

 3. Tabular Models (LightGBM/XGBoost): Surfacing Edge Cases

Tree-based models like LightGBM and XGBoost thrive on edge splits so your synthetic data should emphasize boundaries, transitions, and class boundaries.

Synthetic Data Strategy

1. Analyze real data decision boundaries

   • Train a model on real data

   • Identify misclassified or low-confidence regions

2. Generate synthetic data in the “confusion zones”

prompt = """
Generate 100 records of loan applications with features:
- income: 20000–150000
- credit_score: 300–850
- loan_amount: 5000–100000
- approved: 0 or 1

Focus on edge cases:
- borderline approvals (e.g., income=30k, credit_score=620)
- conflicting features (high income, low credit)
Return as JSON
"""

3. Mix into training set with higher weight

train_data["sample_weight"] = train_data["source"].apply(
    lambda x: 2.0 if x == "synthetic_edge" else 1.0
)

 Result:

• Better model calibration near thresholds

• Improved recall on rare or high-stakes outcomes (e.g., fraudulent loans)

 Evaluation Across Modalities

For each model type, your evaluation should align with data purpose:

Final Thoughts

Good synthetic data doesn’t just look real — it fits the model.

In this post, you saw how to tailor generation for:

• LLMs → focus on instruction-completion shape

• Vision → focus on contrast and semantic alignment

• Tabular models → focus on edge cases and thresholds

When your synthetic data is aligned with model architecture, training becomes more efficient, generalization improves, and debugging gets easier.

That’s not just synthetic data – that’s synthetic strategy.