Overview
One of the biggest bottlenecks to training LLMs is a lack of high-quality, annotated data.
While annotation itself isn’t difficult, most companies don’t have labeled datasets readily available—and few want to invest the time or resources to create them.
Synthetic data generation addresses this gap by creating realistic, structured, and controllable training examples without relying on human labeling.
While synthetic augmentation has long existed in fields like computer vision, the rise of LLMs has unlocked new potential: you can now use one model to generate useful, domain-specific examples for another.
Adaptive ML’s synthetic data engine turns this capability into a repeatable, scalable workflow. It helps teams prototype faster, explore failure modes, and fine-tune production models with high-quality synthetic inputs.
Why it Matters
Data can make-or-break GenAI projects, accelerating or delaying deployment.
Synthetic data flips that dynamic. It lets teams generate training data on demand without manual labeling. This is especially valuable when:
• Bootstrapping early-stage models with minimal or no task-specific data
• Adapting general-purpose models to domain or company-specific logic, tone, or style
• Meeting privacy requirements where real data is off-limits or restricted
• Reducing manual overhead in data collection, labeling, and QA loops
But synthetic data isn’t just about speed—it’s about visibility and control.
Because you can define the logic behind the data with other features like AI Judges, you can test exactly what your model is learning, and push it to generalize in meaningful ways.
Our Workflow
You can begin with just a handful of prompts, examples, or logic rules. These are used to kick off generation—either via self-play (model-to-model dialogue), pattern-based templating, or open-ended sampling.
Our Engine supports multiple generation modes, allowing you to control parameters like novelty, tone, structure, and domain specificity. You can use high-level settings or programmatically define constraints via our SDK.
A small subset of outputs can be reviewed by humans or Adaptive’s built-in AI judge, which has been trained to provide feedback on structure, accuracy, tone, or alignment. This feedback loop can then guide further generation or inform reinforcement learning steps.
Because synthetic data is generated with known logic or structure, you can run targeted evaluations—measuring things like whether the model learns the intended pattern, how it handles variants, or where it fails to generalize. We provide metrics and visual diagnostics to help you tune and iterate with confidence.
All generated datasets are versioned and traceable. You can compare model performance across datasets, track improvements, and push synthetic examples directly into training or fine-tuning workflows.
