Synthetic Data with David Berenstein and Ben Burtenshaw — Weaviate Podcast #118!

6 min readMar 25, 2025

Weaviate Podcast #118 — Synthetic Data with Ben Burtenshaw and David Berenstein!

The AI World’s Shift to Synthetic Data

The landscape of AI development is rapidly transforming, with synthetic data emerging as the critical building block powering the next generation of models. In our latest Weaviate Podcast, we welcomed David Berenstein and Ben Burtenshaw from Hugging Face, who joined after pioneering groundbreaking work at Argilla. Their journey represents the natural evolution from human feedback to synthetic data generation — a shift that’s democratizing AI development.

This conversation comes at a pivotal moment when organizations worldwide are realizing that high-quality training data, not just model architecture, often determines the success of AI implementations. The Synthetic Data Generator UI and Distilabel library represent Hugging Face’s commitment to making these advanced techniques accessible to everyone, not just specialized research teams with extensive resources.

The Evolution from Human Feedback to Synthetic Data

Synthetic data generation emerged organically from human feedback workflows. As Ben explained, “Argilla started off as this human feedback tool… people looking at data and building really high quality datasets.” The team observed how practitioners were increasingly using large language models to generate data that humans would then review.

This cyclical process — generating synthetic data, gathering human feedback, and iterating — became so foundational that it warranted dedicated tooling. Distilabel grew directly from this workflow, implementing algorithms from research papers for data generation while integrating with both API providers like OpenAI and locally-run models.

The key advantage is scale: while human-annotated datasets might contain thousands of examples, synthetic approaches can generate millions of high-quality training samples with appropriate quality controls in place.

The Taxonomy of Synthetic Data Generation

David outlined a comprehensive taxonomy of synthetic data approaches that helps practitioners understand the landscape:

Data Augmentation: Using effective prompting for data labeling, reformation, and rewriting
Data Synthesis: Distilling data directly from models through completions or domain-specific distillation
Post-Training Datasets: Creating instructions, preferences, and critiques where completions are scored against each other

This framework helps teams identify which approach best fits their use case. For classification tasks, data synthesis might be sufficient, while complex dialog systems might require preference data with explicit critiques explaining why one response outperforms another.

The conversation highlighted pioneering research like the Self-Instruct paper from Stanford (which scaled 175 initial samples to 50,000 for Alpaca) and the Ultra Feedback methodology that enabled evaluation frameworks based on helpfulness, conciseness, and effectiveness.

Persona-Driven Generation for Unparalleled Diversity

One of the most fascinating techniques discussed was persona-driven data generation. As Ben explained, “From a persona, you can’t expect the language model to extract knowledge because the persona is such a dense representation… But what we can expect a language model to get from personas is variety.”

The Persona Hub dataset contains a billion synthetic personas extracted from pre-training corpora like Red Pajama. Each persona represents a concise description of a potential user with specific knowledge and interests. For example: “A software developer who is looking for a way to simplify the integration of GRPS technology data into their embedded system designs.”

By generating data from different personas interacting with the same content (like documentation), teams can create datasets with much greater diversity than traditional approaches. This addresses a common problem where synthetic generation gets “stuck” on the most commonly represented aspects of source material rather than exploring edge cases.

Hugging Face has made this technology accessible through “Find Personas,” which allows users to search for clusters of related personas (e.g., engineers) to guide their synthetic data generation.

Systems Engineering for Production-Scale Generation

Generating synthetic data at scale introduces significant engineering challenges. Distilabel addresses these through:

Ray-based distribution for running models across multiple GPUs
DAG-based caching that enables recovery from failures
Direct integration with the Hugging Face Hub for persistent storage
SQL-based exploration of partially generated datasets

As David noted, “Whenever your pipeline breaks down… you would just be able to rerun the entire pipeline from where it left off because everything is loaded again in memory.”

This systems approach is crucial when generating datasets containing millions of examples, where naive approaches would fail due to memory constraints or require restarting from scratch after failures.

Hidden Gems

The conversation revealed several insights that casual listeners might miss but offer significant value:

The “Persona to Personas” technique allows discovering related personas, similar to six degrees of separation in social networks. This enables synthetic data that captures not just individual personas but their relationships, creating multi-agent simulations of interactions between different user types.

The importance of embedding-based diversity checks emerged as a critical quality control technique. Rather than naive deduplication, the team uses semantic clustering to ensure datasets contain a balanced representation of concepts, preventing overrepresentation of common patterns.

Perhaps most intriguing was the revelation that many techniques from synthetic data generation are now moving into pre-training. As Ben noted: “If you look at Lama3’s technical report and SmallLM2, you’ll see that synthetic data goes all the way back to pre-training.” This suggests synthetic data isn’t just for fine-tuning anymore but is becoming foundational to how base models themselves are created.

Practical Takeaways

For Beginners

Start with simple text classification tasks using the Synthetic Data Generator UI. The interface abstracts away pipeline complexity while giving you full control over data characteristics. Begin with 500–1000 examples to test your approach before scaling up. The integration with Hugging Face’s AutoTrain means you can go from zero to a working model without writing a line of code.

For Intermediate Users

Implement persona-driven generation to increase dataset diversity. Try the “Find Personas” feature to identify personas relevant to your domain, then use these to guide generation. This approach works particularly well when you have domain-specific documentation that needs to be explored from multiple user perspectives.

For Advanced Practitioners

Build multi-stage pipelines that incorporate feedback loops. Use embeddings to analyze the diversity of generated data, then adjust your generation strategy to target underrepresented regions in the embedding space. Consider implementing evolutionary approaches from papers like DEITA, where prompts evolve for complexity while being pruned based on quality metrics.

Weaviate-Specific Relevance

The synthetic data generation approaches discussed align perfectly with Weaviate’s upcoming Transformation Agent. As mentioned in the podcast, this allows for durable execution when generating objects — if the process crashes on the eighth object, it resumes from that point rather than starting over.

For Weaviate users working with query agents or developing RAG applications, these techniques offer powerful ways to generate synthetic test data. For example, creating synthetic database schemas from personas can provide realistic test cases for your query planning and optimization strategies.

The embedding-based diversity techniques also complement Weaviate’s vector capabilities, enabling quality control over generated datasets by ensuring appropriate distribution in semantic space — a perfect application for Weaviate’s nearest neighbor and clustering capabilities.

Conclusion

Synthetic data generation has evolved from a specialized research technique to an essential component of the AI development lifecycle. As David and Ben demonstrated, the tools for creating high-quality synthetic datasets are becoming increasingly accessible, enabling teams of all sizes to benefit from these approaches.

Whether you’re looking to train a simple classifier or build complex multi-agent simulations, the techniques discussed provide a roadmap for implementation. We encourage you to explore the Synthetic Data Generator UI on Hugging Face Spaces and consider how these approaches might enhance your own Weaviate implementations.