Governance Confidence | Separating Retrieval Relevance from Decision Suitability | R.A.H.S.I. Framework™

An employee asks an agent whether they qualify for Benefit X. The top-ranked policy is an excellent match and the answer cites it. Yet that policy may be unapproved, superseded, written for another country or employee population, or subordinate to the employee’s contract.

Azure AI Search ranks results for relevance and retrieves documents associated with similar vectors. Microsoft Foundry distinguishes RAG retrieval quality, groundedness and response relevance.

Retrieval relevance ≠ governance confidence.

In R.A.H.S.I., Retrieval Confidence concerns whether relevant information was found. Governance Confidence is the degree of assurance that the authority and governance conditions for the intended decision have been satisfied. It is neither a semantic score nor a native Microsoft metric.

Each transition has a distinct burden:

• Who approved and owns the Benefit X policy?
• Does its mandate cover this employee, country and contract?
• Is this version effective and unsuperseded?
• Is the evidence sufficient for an eligibility determination, and who is permitted to make it?

A relevant source can fail any later test.

For enterprise design, the boundary between explaining a policy, determining eligibility and executing a benefit change must be explicit. Accountable owners define policy authority and decision rights. Consequence level shapes review and release gates; unresolved suitability calls for a qualified answer, more evidence, abstention or human.

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| R.A.H.S.I. Framework™

🛡️ Need implementation, not just insights?

Operationalise Benefit X authority boundaries through owned release gates and reconstructable decision evidence.

Governance Confidence | Separating Retrieval Relevance from Decision Suitability | R.A.H.S.I. Framework™

Separate retrieval relevance from decision suitability with governance controls for grounding, completeness, context, risk and decision evidence.

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