Regulatory Affairs AI

Part II: The Compliance Document Generator Blueprint

Benjamin Arazy

 

Welcome to Part 2.

Now that we’ve covered how many sample documents you really need, it’s time to explore how to design a reliable AI system capable of generating submission-ready regulatory documents — consistently and at scale.

This is your practical blueprint.

1. Define What You Want to Generate

Start narrow and specific. Early focus areas work best when the structure is predictable:

  • KSA labels
  • Risk Analyses
  • ER/GSPRs
  • IFUs, DoCs, CER sections (later stages)

Each document type will require a slightly different strategy, dataset, and validation workflow.

2. Choose Your Architecture

You have two main paths:

Option A: Fine-Tuning Only

  • Requires significantly more examples (25–40+ per document type).
  • Works best when you already have a large archive of consistent historical documents.

Option B: Templates + RAG + Minimal Examples

The most reliable setup for regulatory and compliance-heavy outputs:

  • Structured templates
  • Knowledge base with regulations and rules
  • 5–10 high-quality gold samples

This hybrid architecture provides superior consistency for predictable, regulated documents.

3. Map Your Device Families

Group your devices by technology and risk profile to scale efficiently:

  • Active therapeutic
  • Active monitoring
  • Implants
  • IVD
  • Ophthalmic
  • Disposable
  • Software / AI

You will sample per device family, not per SKU — a key distinction for reducing data requirements.

4. Set Practical Sample Targets

Based on Part 1, realistic targets look like:

  • KSA labels:
    25–40 examples or 5–10 + RAG setup
  • Risk Analysis:
    5–10 samples per device family
  • GSPR/ER:
    10–20 samples across families or 5–10 + requirement library

Always focus on validated, approved, consistent documents.

5. Build Your Knowledge Base

This is the backbone of your RAG pipeline. Include:

  • Global regulations (SFDA, MDR/IVDR, ISO standards)
  • Hazard libraries
  • Requirements & justification libraries
  • Internal SOPs
  • Historical submission data

The richer and more structured your knowledge base, the more compliant the generated output.

6. Standardize Templates

Your AI should fill a structure — not invent one.

Create highly structured templates for:

  • Labels
  • Risk analysis formats
  • GSPR/ER tables
  • IFU skeletons
  • DoC layout

The more standardized your templates, the more reliable the AI’s results.

7. Annotate a Small “Gold Set”

Select your 5–10 best examples and annotate:

  • Why each field exists
  • Device family and classification
  • Inclusion/exclusion rules for hazards
  • Preferred justification phrasing

A small annotated set is far more valuable than a large, messy dataset.

8. Connect Templates to Device Data

If you already use a system like LICENSALE/REGISLATE:

  • Device inputs → template slots
  • AI fills the language
  • RAG injects regulatory rules
  • A validation layer completes the review

This turns your architecture into a powerful, scalable pipeline.

9. Test on Unseen Devices

Always validate using devices not included in your training set.

Check for:

  • Accuracy
  • Completeness
  • Phrasing consistency
  • Regulatory alignment
  • Hazard correctness
  • Applicability

Human RA review is essential here.

10 Iterate and Expand

Identify weak areas:

  • Specific device families
  • Difficult GSPR rows
  • Certain hazard categories

Add a handful of new examples or expand your libraries.
Repeat. Each iteration strengthens your generator.

Final Thoughts

A reliable Compliance Document Generator doesn’t depend on massive datasets.

It depends on:

  • The right examples
  • Highly structured templates5
  • Clear regulatory rules
  • A strong knowledge base
  • A realistic device taxonomy
  • Iterative refinement

Get these components right, and even a small dataset can produce consistent, compliant, scalable regulatory documents.

 

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