Case Study
Value Discovery & Portfolio
Prioritize GenAI investments with defensible stop criteria and risk-aware gating, using resources on initiatives that can scale.
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Generative AICase Study
Generative AI Systems Design
Deploy citation-backed copilots with rigorous retrieval contracts, ensuring answers are verifiable, permissioned, and resistant to hallucinations.
Executive Outcome
01
Verifiable answers through contract-governed retrieval, with each response grounded in traceable source artifacts.
02
Permissioned retrieval: eligibility and access controls applied before ranking and generation, reducing leakage risk at source.
03
Trustworthy behavior through citation discipline and refusal when evidence is missing, reducing hallucinations and unsupported claims.
Engagement focus
Production-grade RAG systems with citation discipline and access control.
Context
Enterprises moving from 'chat with documents' demos to production knowledge assistants. The key constraint was ensuring answers are verifiable, sourced only from permitted documents, and robust against hallucinated content and stale information.
The Challenge
- 01Retrieval treated as a black box, with limited visibility into why content was selected.
- 02Answers lacked reliable citations and occasionally referenced unsupported sources.
- 03Data freshness and source validity were inconsistent across content types.
- 04No systematic mechanism to prevent retrieval of ineligible or unauthorized documents.
Approach
- →Defined a formal Retrieval Contract to govern eligibility, freshness, and citation requirements from query to retrieved chunks.
- →Established grounding and citation policies so that unsupported answers are not emitted (no evidence, no answer).
- →Standardized indexing and chunking strategies by document type, with explicit metadata requirements.
- →Enforced ACLs at the retrieval layer so eligibility is applied before ranking and generation.
Key Considerations
- Grounding and citation checks add latency and require careful performance budgeting.
- Strict citation discipline may reduce fluency in exchange for higher correctness and trust.
- Requires reliable metadata and document lifecycle controls to keep retrieval eligibility accurate.
Alternatives Considered
- ✕Long-context only (no retrieval): rejected due to cost, stale context risk, and lack of auditable source attribution.
- ✕Naive top-K retrieval without eligibility filtering: rejected due to leakage risk and inconsistent answer quality.
Representative Artifacts
01Retrieval Contract Template (Eligibility, Freshness, Citation requirements)
02Indexing & Chunking Decision Framework
03Source Governance Model (Permitted sources, expiry, ownership)
04Acceptance Test Suite (Grounding, citation accuracy, leakage)
05Trace Specification (Retrieval → prompt → response evidence)
Acceptance Criteria
Verified that every substantive assertion in an answer is backed by a cited source.
Verified that users cannot retrieve chunks outside their permissions.
Verified that the system refuses to answer when relevant evidence is missing or ineligible.
Verified that citation links resolve to valid, reachable source artifacts.
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