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Generative AI

Enterprise RAG on Vertex AI: From Documents to Grounded Answers

A delivery-oriented view of enterprise RAG: content governance, Vector Search retrieval, permission-aware answering, and evaluation loops on Vertex AI.

Overview

  • RAG quality starts with governed, deduplicated, and access-controlled content.
  • Retrieval must respect user permissions at query time.
  • Grounded answers need citations, freshness signals, and refusal behavior.

Content

Treat the knowledge base as a governed data product

Document ingestion should record ownership, validity periods, and audience scope. Stale or duplicated content is the most common cause of wrong RAG answers in production.

  • Chunk documents along structural boundaries and keep source anchors.
  • Expire or supersede documents explicitly instead of leaving both versions retrievable.

Retrieval

Permission-aware retrieval with Vector Search

Vector Search narrows candidates by semantics, while access filters narrow them by the asking user's entitlements. Both must happen before Gemini sees any context.

  • Filter by tenant, department, and classification level at query time.
  • Log retrieved chunks per answer for audit and quality review.

Trust

Grounding, citations, and the right to refuse

Production assistants should answer with citations, state when content is missing, and refuse questions outside retrievable scope instead of producing confident guesses.

  • Score answers against retrieved evidence in evaluation loops.
  • Route low-confidence answers to human review channels.