Part II: The Compliance Document Generator Blueprint

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• Works best when you already have a large archive of consistent historical documents.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.