Describe your ML task. Distill generates synthetic training data, measures downstream model performance, and uses reinforcement learning to make the next batch better. A flywheel for data quality.
Text classification, NER, translation, question answering. Describe what you need in plain language or provide a schema.
LLMs produce diverse, domain-specific training examples following your task specification. Thousands of labeled examples in minutes.
A model trains on the generated data. Performance metrics feed back into the system. What worked? What didn't?
Reinforcement learning adjusts the data generation policy based on actual model outcomes. Not heuristics. Not vibes. Measured improvement.
Classification, entity extraction, translation, sentiment. Generate labeled text data optimized for your specific domain and vocabulary.
When real data barely exists, Distill synthesizes training sets that capture linguistic patterns LLMs already understand but can't demonstrate at scale.
Financial records, medical tables, transaction logs. Realistic distributions, proper correlations, no privacy exposure.
Your model fails on rare inputs. Distill learns to generate more of the hard cases that actually improve robustness.
Distill turns model performance into a signal, and that signal into better training data. The loop never stops improving.