Graph-Powered Temporal Validity: Preventing RAG Systems from Citing Dead Regulations
Session track: Modern Applications
Session time:
Session description:
Your compliance RAG system just retrieved a regulation with perfect semantic similarity—but it expired 18 months ago. The vector embedding can't tell the difference between a 2022 circular and the 2024 version that superseded it, because semantic similarity is temporally blind. This temporal contamination failure mode produces confident, coherent answers backed by legally invalid sources, and standard evaluation metrics won't catch it.
In this session, Himanshu will demonstrate how Neo4j knowledge graphs solve this architectural blind spot by modeling regulatory documents as temporal entities with explicit superseding relationships, effective dates, and sunset clauses. You will learn how to build a GraphRAG pipeline that enforces temporal validity as a first-class constraint, blocking expired regulations at retrieval time before they contaminate LLM context. Drawing from controlled research on financial regulatory corpora (RBI Master Directions and circulars) and production deployments that achieved 67% faster audit times and 30% hallucination reduction, he will show the architecture, Cypher queries, and graph data models that prevent this failure.
The session covers structure-aware document ingestion that preserves regulatory provenance, graph-based metadata modeling for versioned documents, pre-generation validity filtering using temporal Cypher patterns, and safe refusal mechanisms when valid coverage is missing. You will see real code, graph schemas for superseding relationships, retrieval patterns that query document status alongside semantic similarity, and operational monitoring strategies that track citation staleness as a safety KPI. Attendees will leave with production-ready blueprints for building temporally-aware GraphRAG systems where "correct" matters more than "complete"—critical for regulated industries where citing dead law carries legal and compliance risk.
Speaker
Himanshu Goel
AI Research Engineer & Data Scientist | GenAI, LLMs, RAG
Himanshu Goel is an AI Research Engineer and Data Scientist with nearly six years of experience building and deploying production-grade AI, machine learning, and Generative AI systems across finance and healthcare. He specializes in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and document intelligence systems, with a strong focus on reliability, scalability, and real-world constraints.
His work spans the full AI lifecycle, including data analysis, feature engineering, statistical modeling, and the design of end-to-end ML and GenAI pipelines using Python, TensorFlow, LangChain, and cloud platforms such as AWS and Azure. Himanshu has led and contributed to enterprise solutions involving semantic search, regulatory document analysis, predictive modeling, and AI-powered decision support systems, delivering measurable improvements in accuracy, efficiency, and cost.
Through his talks, Himanshu focuses on sharing practical lessons from building AI systems in production covering architecture decisions, trade-offs, failure modes, and best practices for deploying trustworthy AI solutions in cloud environments.
