RCTVS AI Hallucination Detection Sequence Diagram

general · sequence diagram.

About This Architecture

Seven-phase sequence diagram orchestrating AI hallucination detection within a Requirements-Code Traceability and Verification System (RCTVS). Requirement Analysts, Developers, and Testers submit artifacts through a Requirement Management Module, Traceability Engine, and AI Verification Engine that performs NLP and semantic analysis to detect inconsistencies. The workflow chains input submission, GitHub integration, requirement capture with version control, RTM generation, AI-powered hallucination detection, change impact analysis, and final report delivery to stakeholders. This architecture ensures requirements remain synchronized with code and tests while identifying AI-generated false claims or contradictions. Fork this diagram on Diagrams.so to customize phases, add additional verification gates, or integrate with your existing CI/CD and requirements management tools.

People also ask

How does an AI hallucination detection system verify requirements against code and identify inconsistencies in a traceability workflow?

This RCTVS sequence diagram shows a seven-phase workflow where Requirement Analysts submit requirements, Developers and Testers provide code and test cases via GitHub, and an AI Verification Engine performs NLP and semantic analysis to detect hallucinations and contradictions. Change Impact Analysis then assesses downstream effects before delivering reports to stakeholders.

RCTVS AI Hallucination Detection Sequence Diagram

Autoadvancedsequence-diagramrequirements-engineeringAI-verificationtraceabilityhallucination-detectionNLP
Domain: Ml PipelineAudience: Requirements engineers and QA architects implementing AI-driven traceability and hallucination detection systems
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Created by

March 2, 2026

Updated

April 2, 2026 at 4:10 PM

Type

sequence

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