Key Features of Synthetic Data Studio

Supervised Fine-Tuning Dataset Generation

• Automatically generates high-quality prompt-completion pairs from raw or redacted documents by creating question-answer sets or summaries.

• Ideal for fine-tuning machine learning models, especially for unstructured content or when data availability is limited.

• Supports the creation of task-specific datasets tailored to enterprise needs.

Document-Based Generation

• Advanced document processing capabilities allow users to generate synthetic data directly from uploaded document collections.

• Ensures the creation of domain-specific datasets that are consistent with existing documentation and knowledge bases.

Custom Generation Workflows

• Offers a flexible workflow system to process user-provided inputs and create tailored data generation pipelines.

• Guarantees that the generated data aligns perfectly with specific use cases and enterprise requirements.

Evaluation Workflow

• Supports the evaluation of generated supervised fine-tuning (SFT) datasets using an LLM-as-a-judge.

• Provides evaluation scores and detailed justifications for each generated prompt-completion pair.

• Allows you to define custom scoring criteria and justification parameters for precise quality assessments.

• Ensures datasets meet the highest quality standards before being used for fine-tuning.

Export Functionality

• Enables datasets to be exported to the Project File System for easy access.

• Supports seamless uploading of datasets to Hugging Face for broader accessibility and further model training.