Synthetic Data Generation — create training data with LLMs

Use strong LLMs to generate diverse, high-quality training examples for fine-tuning, then filter and verify quality before use in model training.

Data4.3

Synthetic Data Generation — create training data with LLMs preview

Prompt template

Generate a diverse training example for the following task. The example should be realistic, high-quality, and cover a scenario not yet in the dataset.

Task description: {task_description}

Existing topics to avoid: {existing_topics}

Generate:
Input: {input}
Output: {output}
Reasoning (for CoT data): {reasoning}

How to use

1Define the target task, desired input/output format, and quality criteria for synthetic examples
2Seed the generator with a small set of real examples (10-50) to establish style and distribution
3Use a strong teacher model (e.g. GPT-4, Claude) to generate examples in batches with topic diversity prompts
4Apply deduplication (embedding similarity >0.95) to remove near-duplicate generated examples
5Filter quality using a combination of heuristic checks (length, format) and LLM-as-judge scoring
6Optionally have a human review a random sample (5-10%) to validate overall quality before training

Pro tips

Synthetic data works best when combined with real data — aim for 30-70% synthetic depending on task
Use 'seed tasks' from frameworks like Evol-Instruct to generate progressively more complex examples
Always deduplicate against your evaluation set — synthetic data leakage invalidates benchmark scores
Distilabel and Argilla provide pipelines for generation, filtering, and human-in-the-loop review

Works best with

distilabelargillahuggingfaceChatGPT

Ratings & Reviews

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Related Skills

Fine-tuning

LoRA / QLoRA Fine-tuning — adapt models without retraining

Inject low-rank adaptation matrices into transformer layers to fine-tune a model on domain-specific data using a single consumer GPU.

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4.6