DeepLearning.AI 预告 AI Dev NYC 2026：Neo4j 团队主讲 GraphRAG 主题
Neo4j 三位开发者要在 AI Dev NYC 2026 上讲 GraphRAG，用图数据库给模型喂数据提升准确性，还有工作坊教你排查智能体信错证据的情况。做 RAG 和智能体开发的人可以关注下这个议程。
DeepLearning.AI 宣布 AI Dev NYC 2026 将于 2026 年 11 月 30 日至 12 月 1 日在纽约 Chelsea Piers 举办，核心内容之一是由 Neo4j 的 Stephen Chin、Nyah Macklin 和 Jennifer Reif 主讲的 GraphRAG 专场。
该演讲聚焦如何用图数据库为模型提供上下文，解决模型输出准确性依赖所给数据的问题。主办方还安排了一场动手工作坊，主题是识别智能体采信了不应信任的证据的情形，属于 GraphRAG 在智能体可靠性方向的实操内容。
活动信息与购票入口通过 Luma 平台（luma.com/AIDevNYC2026）发布。
A model is only as accurate as the context you give it. Stephen Chin, Nyah Macklin, and Jennifer Reif of @neo4j teach grounding models with data at AI Dev NYC: a GraphRAG talk on the main stage, plus a hands-on workshop on catching when your agent trusts evidence it shouldn’t.. 📅Nov 30–Dec 1, 2026 · Chelsea Piers, NYC 🎟Get your tickets luma.com/AIDevNYC2026?u… dP #AIDev e #DeepLearningAI A #GraphRAG AG📊 1 ⚡
This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include GraphRAG, Neo4j, DeepLearning.AI, RAG.
Source attribution: curated from DeepLearning.AI (@DeepLearningAI).
