Creating a Data Curation Run
Curate high-quality synthetic data with flexibility and precision using the Collinear AI Platform. Learn about amplification, supported models, and quality criteria.
🚀 Creating a Data Curation Run
The Collinear AI Platform makes it effortless to create synthetic datasets tailored to your exact needs. This guide walks you through setting up a Data Curation Run — from selecting generator models to defining quality criteria.
🎥 Interactive Walkthrough
Want to see it in action? Follow this guided demo to create your curation run:
🧪 What is Synthetic Curation?
Synthetic curation is the process of automatically generating diverse, high-quality examples to power model training, evaluation, or fine-tuning. With Collinear AI, you can amplify your dataset while maintaining control over quality, diversity, and relevance.
Key features:
- Scale fast: Generate hundreds to thousands of samples with minimal effort.
- Maintain precision: Define exactly what quality looks like for your use case.
- Stay flexible: Customize generation settings and quality filters.
🔥 Amplification Factor
The Amplification Factor determines how much you expand your data:
- Higher amplification → More synthetic samples per input.
- Lower amplification → Fewer, more targeted samples.
This setting allows you to balance quantity and curation precision based on your project’s needs.
🧠 Supported Generator Models
Collinear AI supports the most powerful and versatile models for synthetic data generation:
- GPT-4o — OpenAI’s flagship model, excelling at reasoning, creativity, and instruction-following.
- LLaMA 70B — Optimized for balanced performance and efficiency.
- LLaMA 405B — Massive-scale model offering unparalleled depth and nuance.
- Qwen 72B — Strong multilingual and instruction-following capabilities, ideal for diverse domains.
Select the model that best matches your data complexity, domain, and scale requirements.
🛠️ Curation Quality Criteria
To ensure every piece of generated data meets high standards, Collinear AI allows you to curate outputs based on these key dimensions:
- Correctness: How accurately outputs match desired criteria and ground truth examples.
- Naturalness: How well outputs mimic real-world language patterns and tone.
- Diversity: The range and uniqueness of generated variations, ensuring broad coverage.
- Coherence: Logical and semantic flow throughout the output.
- Instruction Following: Adherence to the specified prompts and task instructions.
- Quality of Reasoning: Depth, logic, and coherence in the explanations and conclusions.
You can apply one or multiple curation filters to align the dataset with your specific needs.
✅ Best Practices for Successful Curation Runs
- Mix Quality Filters: Combine multiple curation criteria for a balanced dataset.
- Tune Amplification: Start small and adjust amplification as you analyze generation patterns.
- Spot-Check Outputs: Always manually review a subset of samples to validate automatic curation.
Following these steps ensures you achieve high-quality, task-aligned, and scalable synthetic datasets every time.
🎯 Next Steps
Ready to start? Head to your workspace and create your first curation run today!