AI Dev NYC workshop targets agents that cite real-but-inapplicable evidence
DeepLearningAI · x · 2026-11-13
DeepLearning.AI promotes a hands-on AI Dev NYC 2026 workshop (Nov 30–Dec 1, Chelsea Piers) led by Neo4j developer advocate Jennifer Reif and Nyah Macklin on a common retrieval failure: basic similarity search finds text that looks relevant without checking whether it applies to the context — like an agent justifying a wrong refund decision with a real but outdated policy. The session covers diagnosing such agents, fixing them with the simplest approach that works, modeling graph connections across customer/product/policy data, and knowing when an agent should stop.
Related event: AI Dev NYC 2026 Workshop Tackles Agents Citing Inapplicable Evidence(2 posts)→
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