GraphRAG & Explainable AI: Building Trustworthy LLM Outputs
Most enterprise LLM failures aren’t technical — they’re trust failures. Models hallucinate, drift from source truth, or produce outputs with no provenance. For regulated industries, that’s unacceptable. This session introduces GraphRAG — a breakthrough approach combining knowledge graphs (Neo4j) with retrieval-augmented generation to deliver traceable, explainable, and auditable AI outputs. You’ll learn how to design, evaluate, and deploy GraphRAG architectures aligned with the EU AI Act, NIST AI Risk Management Framework, and enterprise AI governance standards.
Problems Solved
- LLM answers without evidence or traceability
- Stale or inconsistent retrieval data
- Non-compliance with transparency and provenance regulations
- Lack of explainability for model outputs
- Low confidence from regulators, auditors, and executives
Why Now
- Enterprise AI adoption slowed by lack of trust and explainability
- Regulations (EU AI Act, NIST AI RMF) now require provenance and model transparency
- Executives demand evidence-based reasoning, not black-box answers
What GraphRAG Is
- Combines knowledge graphs (Neo4j) with retrieval-augmented generation
- Returns answers with structured evidence paths — connecting entities → relationships → source documents → LLM response
- Goes beyond flat vector search to capture contextual meaning, hierarchy, and causality
Where It Applies
- Insurance: Claims approvals and denials with transparent justification
- Healthcare: Patient summaries with provenance and compliance
- Finance: Audit trails, credit-risk reasoning, regulatory reporting
- Policy & Legal: Regulatory interpretation and case law summaries
Why It’s Valuable
- Establishes trust with executives, auditors, and regulators
- Improves faithfulness, groundedness, and transparency of model outputs
- Reduces disputes, compliance risks, and hallucination-related rework
- Creates structured AI reasoning pipelines aligned with governance frameworks
Agenda
Opening & Problem Context
Why trust is the bottleneck for enterprise AI.
Examples of LLMs failing in regulated use cases — what breaks when outputs lack provenance.
Pattern 1: Anatomy of GraphRAG
Understanding how GraphRAG extends RAG with Neo4j graphs.
Schema design for entities, relationships, and evidence paths.
Structured retrieval from graph → vector → generator.
Pattern 2: Architecture & Data Flow End-to-end GraphRAG blueprint: Ingestion → Entity extraction → Graph population → Retrieval orchestration → Response grounding. Contrast with plain RAG and vector-only approaches.
Pattern 3: Explainability & Evaluation Metrics for evaluating explainability: Faithfulness, groundedness, and coverage. How to trace model answers back to graph nodes and documents. Integration with AI observability platforms (PromptLayer, Arize, etc.).
Pattern 4: Compliance & Governance Alignment Connecting GraphRAG design to regulatory frameworks:
- EU AI Act: Transparency, traceability, human oversight
- NIST AI RMF: Trustworthiness and accountability
- ISO 42001: AI Management Systems Implementing provenance tags and explainability layers as compliance enablers.
Pattern 5: Real-World Scenarios Industry case patterns:
- “Why was this insurance claim denied?”
- “Which regulation does this contract violate?”
- “Which patient data contributed to this summary?” Each example maps relationships, evidence, and trace paths through Neo4j.
Wrap-Up & Discussion Recap of GraphRAG architecture and design patterns. Checklist for adoption: schema templates, metrics, and governance integration. Q/A and enterprise discussion on explainable AI roadmaps.
Key Framework References
- Microsoft GraphRAG: Open-source structured hierarchical retrieval pattern
- Neo4j Graph Data Science & LLM Integration Guide
- EU AI Act & NIST AI RMF: Provenance, explainability, and risk transparency
- ISO/IEC 42001: AI governance and management principles
- Gartner & Forrester: Trust and transparency as core adoption barriers
Takeaways
- GraphRAG design blueprint (schema + ingestion + retriever)
- Evaluation metrics: faithfulness, groundedness, coverage
- Reference architecture diagrams for Neo4j + RAG + LLM stack
- Playbook for integrating explainability with compliance frameworks
About Rohit Bhardwaj
Rohit Bhardwaj is a Director of Architecture working at Salesforce. Rohit has extensive experience architecting multi-tenant cloud-native solutions in Resilient Microservices Service-Oriented architectures using AWS Stack. In addition, Rohit has a proven ability in designing solutions and executing and delivering transformational programs that reduce costs and increase efficiencies.
As a trusted advisor, leader, and collaborator, Rohit applies problem resolution, analytical, and operational skills to all initiatives and develops strategic requirements and solution analysis through all stages of the project life cycle and product readiness to execution.
Rohit excels in designing scalable cloud microservice architectures using Spring Boot and Netflix OSS technologies using AWS and Google clouds. As a Security Ninja, Rohit looks for ways to resolve application security vulnerabilities using ethical hacking and threat modeling. Rohit is excited about architecting cloud technologies using Dockers, REDIS, NGINX, RightScale, RabbitMQ, Apigee, Azul Zing, Actuate BIRT reporting, Chef, Splunk, Rest-Assured, SoapUI, Dynatrace, and EnterpriseDB. In addition, Rohit has developed lambda architecture solutions using Apache Spark, Cassandra, and Camel for real-time analytics and integration projects.
Rohit has done MBA from Babson College in Corporate Entrepreneurship, Masters in Computer Science from Boston University and Harvard University. Rohit is a regular speaker at No Fluff Just Stuff, UberConf, RichWeb, GIDS, and other international conferences.
Rohit loves to connect on http://www.productivecloudinnovation.com.
http://linkedin.com/in/rohit-bhardwaj-cloud or using Twitter at rbhardwaj1.