Hallucination mitigation, business rules, and approval

Overview

• Hallucination mitigation reduces unsupported model claims using evidence, constraints, validation, and abstention.
• Business-rule validation checks authoritative domain requirements.
• Human approval permits a specific action after a person reviews its concrete details.

Why this matters

A plausible generated decision can still be financially or operationally wrong.

An everyday example

A junior employee can draft a decision, but the rulebook and authorized reviewer determine whether it takes effect.

A technical example

AI suggests missing claim fields; code checks policy ownership and date rules; the customer confirms the exact submission.

How it works

1. Retrieve current evidence
2. Generate bounded proposal
3. Verify claims/rules
4. Ask for necessary review
5. Commit through trusted service
6. Explain receipt.

Architecture and flow

Evidence → AI proposal → rules → approval → authoritative commit.

The insurance assistant example

A model cannot deny a claim or promise a payout solely from its own interpretation of a policy paragraph.

When to use it

Grounded answers and consequential operations.

When to avoid it

Do not use model-generated confidence or another model's agreement as sufficient approval.

Interview answer

“I reduce unsupported answers with source grounding and abstention, while critical decisions are enforced by business rules and authorized review.”

Benefits, trade-offs, and production considerations

• Failures must be visible: unsupported answer → abstain; rule violation → reject; missing approval → pause; uncertain commit → reconcile.
• Never convert these into a generic successful chat response.

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