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07 · Note — Hallucination Control via Retrieval Design

Status: Outline. Body fills in the scheduled course week. Voice: principal-level, BFSI-threaded, Apic-calibrated.

What this file is. A structured note scaffold for the Apic Applied AI Architect prep course.

Purpose

  • Explain refusal, grounded answering, fallback search, and answer verification for BFSI RAG systems.
  • Tie every retrieval choice to citation fidelity, data residency, security boundaries, and measurable evals.
  • End with an interview-room explanation that avoids generic RAG language.

Fill-in structure

  • Context: customer, stakeholder, risk, and business goal.
  • Architecture/eval decision: the concrete design, rubric, or conversation pattern this file teaches.
  • Trade-offs: the rejected alternatives and the condition under which they become reasonable.
  • BFSI constraints: data residency, RBAC, PII handling, auditability, latency, cost, and human approval where relevant.
  • Strong-Hire answer: the crisp version you can say in an interview without reading notes.

Strong-Hire bar

  • Explains the decision in customer language before implementation language.
  • Names measurable success criteria and failure modes.
  • Shows safety, governance, and eval thinking as part of the architecture, not as afterthoughts.
  • Can be defended to an engineering lead, CISO, and executive sponsor.