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Policy Q&A with seed RAG

Ask policy questions and get grounded answers from in-app documents — with citations.

Document Q&AGovernance
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You're answering a policy question from the employee handbook — check citations or an honest gap when nothing relevant is found.

Policy help desk

Handbook answers with citations when evidence exists.

HR business partnerHandbook question
Live
Indexed corpus5 documents
  • pol-001Remote Work Policy
  • pol-002Data Classification Standard
  • pol-003AI Acceptable Use
  • pol-004Incident Response SLA
  • pol-005Vendor Security Review
Scenario: PII in external AI
SharePoint · Policy libraryConfidentialAI governance
Corpus
Internal policy index (142 docs)
Top match
AI Acceptable Use (pol-003)
Classification
Confidential
Last indexed
2026-06-08
Asked by
Support · tier-1

Employee question

Case studyArchitecture, governance, and how to adapt this pattern in a pilot

Business use case

Problem

Teams want a governed Q&A assistant before they stand up vector infrastructure. Pilots still need citations and honest “I don’t know” behaviour.

Who benefits

  • Compliance & legal, answers tied to named policy documents
  • HR / IT, self-serve policy questions without ticket volume
  • Platform engineering, a portable pattern that swaps seed search for Azure AI Search, Vertex AI Search, or pgvector later

Success metrics

  • ≥ 90% of pilot answers include at least one cited document title
  • “Unknown” rate tracked when retrieval returns no hits
  • Time-to-first-demo under two weeks using seed corpora

Solution

Retrieve top policy chunks with keyword scoring over shared seed documents, then call generateText with cite-only instructions. Same seeds power Azure and AWS examples for apples-to-apples comparisons.

Technical implementation

Stack

  • AI SDK generateText
  • Shared seeds in lib/demos/seed-documents.ts
  • OpenAI or AI Gateway via getLanguageModel()

Architecture

Lightweight RAG in production, retrieve from seeds, generate with strict cite-only instructions.

How it runs
Drawing the flow…

Outcomes and learnings

Prove grounding and citation discipline on seeds before you pay for embeddings and index ops.

  • Keyword search is enough for pilot governance conversations
  • Return retrieval metadata so reviewers know when answers are under-informed
  • Unify document IDs across clouds before production indexing
Delivery playbookDiscovery → pilot → scale
  1. 1
    Discovery2–4 wks

    Inventory policy corpora and citation requirements; agree on unknown-answer behaviour when retrieval is empty.

  2. 2
    Pilot6–8 wks

    Ship seed RAG to HR/compliance reviewers; measure citation rate and override frequency.

  3. 3
    Scaleongoing

    Replace keyword search with managed vector index; keep document IDs stable for audit.

Where else this appliesLightweight retrieval plus cite-only generation is the right first step wherever answers must be tied to approved text, not model memory.

HR and people policies

Benefits, leave, and conduct questions with citations to the employee handbook.

Security standards

Developers ask about approved libraries and data handling; answers link to internal standards pages.

Sales enablement

Competitive battlecards and pricing guardrails grounded in official GTM docs.

Manufacturing SOPs

Operators query maintenance procedures on the plant floor with source IDs for audit.

Start in production with seed or keyword search; graduate to pgvector, Turbopuffer, or an enterprise search index without changing the API contract shown here.