Enterprise AI Analysis
PRISM: Proof-Carrying Artifact Generation through LLM × MDE Synergy and Stratified Constraints
PRISM unifies Large Language Models with Model-Driven Engineering to generate regulator-ready artifacts and machine-checkable evidence for safety- and compliance-critical domains.
Key Impact Metrics
PRISM delivers significant improvements in efficiency, correctness, and auditability across safety-critical domains.
0%
Reduction in Manual Remediation
0%
Structural Correctness
0
Average Repair Iterations
0%
Audit Coverage
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Setting the Stage for PRISM
An overview of Model-Driven Engineering, Large Language Models (LLMs), and the existing pain points in current MDE pipelines, highlighting the need for a new approach to safety-critical artifact generation.
The UMM-ICM-CVG Framework
Detailed explanation of PRISM's core architecture, including the Unified Meta-Model (UMM) for semantic backbone, the Integrated Constraint Model (ICM) for constraint aggregation, and Constraint-Guided Verifiable Generation (CVG) for enforcement and repair.
Rigorous Performance Validation
Analysis of PRISM's performance on single-file and multi-file AUTOSAR component generation, assessing structural correctness, semantic consistency, repair efficiency, and cross-domain transferability in legal contexts.
Future Directions and Impact
Summary of PRISM's contributions to verifiable artifact generation, discussion of current limitations, and outlines for future work, emphasizing incremental validation, fine-grained strategies, and audit-guided fine-tuning.
Enterprise Process Flow
Unified Meta-Model (UMM)
→
Integrated Constraint Model (ICM)
→
Constraint-Guided Verifiable Generation (CVG)
80%
Reduction in Routine Artifact Construction through PRISM's Automation
| Metric | Baseline (LLM-only) | RAG | PRISM |
|---|---|---|---|
| Legal Correctness | 0.200 | 0.300 | 0.467 |
| Schema Compliance | — | — | 1.000 |
| Rule Satisfaction | — | — | 1.000 |
Case Study: Brussels I bis Regulation
PRISM was applied to Private International Law (PIL) jurisdiction determination under the Brussels I bis Regulation. This cross-domain evaluation showcased the architecture's transferability, demonstrating layered constraint enforcement and improved doctrinal validity compared to LLM-only and RAG approaches.
Key Takeaways:
- Higher Legal Correctness (0.467 for PRISM vs. 0.200/0.300 for Baseline/RAG)
- Perfect Schema-Pass and Rule-OK (1.000)
- Enhanced Promotion Accuracy (0.933)
Calculate Your Potential ROI with PRISM
Estimate the time and cost savings PRISM could bring to your organization by automating safety-critical artifact generation.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your Roadmap to Verifiable AI Integration
A phased approach to integrate PRISM into your existing MDE and compliance workflows.
Phase 01: Meta-Model & Constraint Foundation
Establish the Unified Meta-Model (UMM) from existing schemas and natural language specifications. Integrate core structural and semantic constraints into the ICM, ensuring auditable provenance.
Phase 02: Constraint-Guided Generation Pilot
Deploy PRISM for single-artifact generation in a pilot domain, leveraging Layer-1 enforcement for structural correctness and Layer-2 validation for semantic compliance. Train and onboard core engineering teams.
Phase 03: Multi-File System & Audit Integration
Scale PRISM to multi-file systems, addressing cross-file dependencies and global invariants. Integrate audit trails and machine-checkable evidence with existing certification processes.
Phase 04: Audit-Guided Repair & Continuous Improvement
Implement Audit-Guided Repair (AGR) for automated and human-in-the-loop repair. Continuously refine constraint models and generation strategies based on feedback and performance metrics.
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