Talk to Your Graph: A Practical Guide to Building a Dual-LLM Q&A System for a Fashion Graph

Connecting LLMs to structured knowledge graphs often leads to unreliable queries and erodes trust in AI-driven analytics. How can developers build conversational systems that are not just powerful but also predictable and accurate?

In this session, Vaskya and Celine will show you their practical end-to-end solution. They will guide you through a system, built from scratch with Python, that transforms a raw fashion dataset into a fully interactive knowledge graph using Neo4j and Google Gemini. The talk will first unveil a novel technique where an LLM acts as an “”AI Architect”” to automatically design the graph schema. Then, you will get a deep dive into the core of their Dual-LLM Q&A architecture, where one AI agent translates questions into high-accuracy Cypher using “”Golden Rule”” constraints, and a second agent narrates the data back into human-readable insights.

By the end of this session, you will learn how to:
– Architect a reliable Dual-LLM chain for separating query generation from summarization
– Implement constraint-based prompting to enforce guardrails and dramatically improve Cypher accuracy
– Automate the initial, time-consuming graph modeling process using an LLM
– Build a complete, conversational application by integrating Neo4j and Google Gemini

Speakers: Vaskya Nabila & Celine Auriel

Resources:
Get Started with Aura – https://bit.ly/3LOLrjh
Deployment Center – https://bit.ly/4jOelM3
Ground AI Systems and Agents with Neo4j – https://bit.ly/4oVsnyb

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