As generative AI reshapes industries, the quality, diversity, and safety of the data used to train and evaluate models have never been more critical. Today, we’re thrilled to announce two major additions to YData's product portfolio that address these growing needs:
- Synthetic Question & Answer (Prompt-Response) Generation
- Synthetic Document Generation (PDF, DOCX, or HTML formats - more to come!)
These capabilities empower teams to create rich, realistic, and privacy-compliant datasets for large language models (LLMs), document intelligence, and agentic AI applications—at scale and on demand.
Synthetic Q&A (Prompt-Response) Generation
Question-Answer pairs (also known as Prompt-Response) are a foundational format for LLM training, fine-tuning, and evaluation. But sourcing high-quality Q&A data in specialized domains can be slow, expensive, and fraught with privacy concerns.
With YData you can now automatically generate synthetic Q&A / Prompt-Response pairs derived from your documents, enabling use cases like:
- LLM Evaluation
Generate evaluation sets to assess how well models answer domain-specific questions, including but not limited to medical advice, legal compliance, financial regulations, internal product knowledge and many other highly specific domains.
- Red Teaming and Safety Testing
Create synthetic prompts that simulate edge cases or adversarial inputs to stress-test model behavior, identify hallucinations or bias and improve safety guardrails.
- Contextual Q&A Generation from Documents
Instead of manually crafting examples, generate grounded questions directly from contracts, reports, or other documents. Benefits include high fidelity to real use cases, efficient generation of hundreds of Q&A pairs per document and elimination of manual annotation overhead.
- This is just the start! There's more than can be done:
- Improving retrieval-augmented generation (RAG) pipelines
- Simulating helpdesk or assistant conversations
- Generating FAQs or chatbot responses for customer support
All Q&A pairs are synthetically generated, domain-aware, and free of real user or business data—making them ideal for sensitive applications.
Synthetic Document Generation: PDF, DOCX, or HTML
Documents like invoices, clinical reports, or contracts are central to most business processes—but they’re also highly sensitive and often hard to use for training AI models.
With YData's new document generation capabilities, you can generate high-quality synthetic documents in your format of choice—PDF, Word (DOCX), or HTML. No manual formatting or HTML conversion needed.
Why Synthetic Documents?
Real documents are often locked behind legal or privacy constraints. Synthetic documents:
- Bypass compliance hurdles
- Provide full control over structure and content
- Can be massively scaled and customized to represent different use cases, formats, and languages
Use Cases:
- LLM Pretraining & Fine-tuning on domain-specific corpora
- Document Classification & OCR tasks that need layout diversity
- Template Testing for document parsers and intelligent data extraction tools
- AI Agent Evaluation on structured/unstructured document understanding
By mixing synthetic documents into your training corpus, you can:
- Improve model robustness
- Fill gaps in underrepresented document types
- Simulate long-tail edge cases that real data rarely captures
Privacy-First. Enterprise-Ready.
Whether you're building internal copilots, compliance tools, or verticalized LLMs, these new features are built with:
- Privacy by design: No leakage, no re-identification risks
- Scalability and flexibility: Run anywhere, at any scale
Get Started Today
The new features are available now in the latest version of the YData SDK. Modules to explore:
- ydata.synthesizers – for Question-Answer / Prompt-Response datasets
- ydata.synthesizers.text.model.document – for generating PDF, DOCX, or HTML files with a single command
Explore YData's SDK documentation for tutorials, examples, and best practices.