Synthetic Data Studio use cases

Explore key use cases where Synthetic Data Studio can help drive innovation, such as model fine-tuning, knowledge distillation, and custom data curation, empowering businesses to build smarter, more efficient AI solutions..

Use Case 1: Enhancing Customer Support LLMs with Privacy-Compliant Training:

Synthetic Data Studio (SDS) addresses the challenge of distilling knowledge from frontier large language models (LLMs) while adhering to strict data privacy regulations. By generating synthetic customer support interactions and analytics, SDS enables the distillation of cloud-based models and fine-tuning of smaller, faster models like Meta-Llama-3.1-8B-Instruct, achieving a 70% win rate over a baseline Goliath-120B model in real-world evaluations without exposing customer data. This approach ensures high-quality training without exposing sensitive customer data.

Use Case 2: Generating Structured Data adhering to the original dataset statistical characteristics.

SDS solves the problem of approximating the statistical properties of real-world datasets for analytics and modeling, particularly in scenarios where raw data is restricted or insufficient. Using clustering and seed instructions, SDS creates synthetic tabular data (e.g., financial sensitive data) that preserves distributions, correlations, and business rules. This ensures synthetic data aligns with original datasets in metrics like mean, standard deviation, and KL divergence, enabling privacy-compliant analysis and model training.

Use Case 3: Accelerating LLM Training for Coding Tasks with Synthetic Code

SDS curates large coding datasets to improve code generation LLMs. It synthesizes code questions, solutions, and unit tests for automated testing. Fine-tuning coding models with such data reduces coding generation errors.

Use Case 4: Scaling Evaluation of LLMs, RAG Systems, and Agents

Manual evaluation of LLMs for various tasks such as coding is time-intensive. SDS automates this process by generating synthetic tasks, tests, and using LLM-as-a-judge prompts to validate outputs at scale. SDS also allows humans to filter out bad samples by inspecting or testing the generated data.