🚀 DeepFabric: a new way to generate synthetic datasets for AI
The DeepFabric technology proposes a different approach to creating training data:
- 🧠 Graphs of diverse topics to cover a domain without repetition.
- 🛠️ Real execution of tools to avoid simulated outputs and minimize hallucinations.
- 📊 Strict validation to ensure each example provides real value.
- 🤝 Open source and community-oriented.
💡 Use cases#
- 🤖 Training conversational models with varied and realistic data.
- 🧾 Generating datasets for autonomous agents that need to interact with real tools.
- 🧪 Robustness testing for models that require broad, non-repetitive scenarios.
- 🏭 Industrial automation where reliable synthetic data is needed to simulate processes.
- 📚 Education and prototyping for teams that want to experiment without relying on sensitive data.
🧩 Explanation in short#
If you need to train an AI model but don’t have enough data, or your data is repetitive, DeepFabric generates varied and realistic synthetic data.
It uses real tools, validates each example, and prevents the model from learning incorrect patterns.
The result: more robust models with fewer errors.
More information at the link 👇
Also published on LinkedIn.
