Metadata-Grounded Retrieval for AI-Driven Field Discovery in Enterprise Data Platforms

Abstract

A field-discovery agent that hallucinates a schema field is not a minor annoyance; it sends an enterprise customer down a path that does not exist. This article argues that grounding quality, not generator scale or prompt engineering, is what actually determines whether a retrieval-augmented field-discovery system can be trusted in production, and that grounding has to be built into the retrieval architecture itself rather than requested through instructions a model may or may not follow. The evidence comes from a production deployment built for an enterprise experience-data platform, where natural-language field discovery had to operate across customer metadata models containing thousands of fields spread across schemas, datasets, and sandboxes. The architecture treats schema structure, organizational context, and field-level permissions as retrieval signals evaluated before ranking, not filters applied after generation, and adds a validation layer that checks every field reference in a draft response against live metadata before that response reaches the user. Measured against a semantic-similarity-only baseline, the architecture reduces unverifiable field references and strengthens permission compliance, consistently across evaluation runs though reported here qualitatively rather than as a single benchmarked figure, at a latency cost the underlying vector-search infrastructure absorbs without difficulty. The deployment also coincided with a reduction in enterprise onboarding time from approximately two months to two weeks, a deployment-level outcome attributable to the broader discovery capability. Architectural grounding, this article suggests, produces more durable reliability than tuning the generator ever will, a claim with direct consequences for where engineering effort should go when hallucination shows up in production AI-discovery systems.