Why Metadata Makes GenAI Retrieval Trustworthy
Steve and Maya break down why vector search alone can surface the wrong answer, and how metadata enrichment adds context, security, provenance, and recency to improve retrieval. They also preview a hands-on workshop covering schema design, controlled vocabularies, hybrid search, citations, and drift monitoring for enterprise GenAI.
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Chapter 1
The Vector Search Illusion
Simon Carver
Welcome to Part Six of the “Making Your Solution Data GenAI Ready” certification series: Pre-Processing and Enriching Your Data — Metadata Enrichment.In this course, we’ll focus on a critical question: is your data rich enough for GenAI to understand and use correctly?Even well-prepared data can fall short if it lacks meaningful metadata. Without the right context, GenAI systems may struggle to retrieve the right information, reason accurately, and produce outputs people can trust.Across five deep-dive sections, you’ll learn how to enrich data with contextual metadata, link internal and external data sources, add descriptive and semantic layers, improve usability for downstream GenAI tasks, and maintain metadata integrity as your data evolves.By the end of this workshop, you’ll have practical approaches for making your data more understandable, connected, reusable, and trustworthy for GenAI solutions.Let’s get started.