Part I: Rethinking Training Data for Regulatory AI
For years, regulatory professionals have worked under an assumption inherited from other AI disciplines: more data means better performance. In OCR, contract analysis, or social-media classification, this principle largely holds. But regulatory documents are not ordinary documents. They are structured, rule-driven, and unforgiving.
This changes the logic entirely. When teams begin building AI systems for regulatory work, they all ask the same question:
How many examples do we need to train a Compliance Document Generator?
Twenty?
Two hundred?
Two thousand?
The honest answer is more nuanced and very different from what many assume.
Regulatory documents aren’t creative writing. You’re not training an AI to be Shakespeare. You’re training it to be the world’s most consistent regulatory assistant. And that shift redefines what “enough data” truly means.
How do people commonly think about Training Data?
1. The Big-Data Approach
Many teams assume they need hundreds or thousands of examples.
This mindset comes from use cases where variability is high and structure is weak — invoices, emails, random contracts, or customer support transcripts.
But regulatory documents follow predefined rules, standards, and templates. Their structure is not random; it’s intentional. Applying big-data logic here dramatically overestimates the complexity.
2. The Quality-First Approach
Others argue you need dozens, not thousands, of examples. And they’re usually right. Because regulatory documents follow templates, standards, and predetermined logic, the model doesn’t need to “learn” creativity. It needs to learn:
- Structure
- Regulatory phrasing
- Conditional logic
- Typical justifications
- Minor variations across manufacturers
In practice, 25–40 high-quality examples per document type are often enough to teach these patterns effectively.
3. The Hybrid Template + RAG Approach (Where RegTech Is Going Next)
Modern RegTech platforms are moving toward a hybrid model:
- Templates define document structure
- RAG (Retrieval-Augmented Generation) injects real regulatory rules
- 5–10 gold-standard examples teach tone, style, and phrasing
This dramatically reduces training requirements. Organizations with strong internal processes can achieve high reliability with far less data than expected.
What “Enough Data” looks like in practice?
1. KSA Product Label (SFDA)
A highly structured, rule-based document.
- 25–40 examples → good for fine-tuning
- 5–10 examples + templates + SFDA rules → best results with minimal data
Because the content is predictable, the intelligence lies in the rules, ot in hundreds of past labels.
2. Risk Analysis (ISO 14971)
This is different. Risk analyses vary not by manufacturer but by technology family. You do not need one RA per product, but you do need coverage across representative technologies:
- Active devices
- Monitoring devices
- Disposables
- IVDs
- Implants
- Ophthalmic
- Software/AI
A realistic training range:
- 5–10 high-quality examples per device family
- Plus a robust hazard & control library
The library is as important as the examples.
3. Essential Requirements / GSPR
Much simpler than risk analysis because:
- The requirements list is fixed
- Table structure is standard
What varies is the applicability and justification wording.
You need:
- 0–20 strong examples across device categories, or
- 5–10 gold-standard examples + requirement library + template
The template and requirement library do most of the heavy lifting.
What comes next?
In Part 2, we will share a practical blueprint used inside modern RegTech platforms to design a reliable, auditable Compliance Document Generator:
- How to structure your data
- How to define templates
- How to integrate regulatory rules with RAG
- How to control style, tone, and consistency
- How to validate and monitor outputs
If you’re building or evaluating AI systems for regulatory work, this blueprint will help you separate marketing claims from real operational reliability.
Stay tuned, the checklist arrives tomorrow.
