LLMs as Drafting Tools, Not Sources of Record

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1LLMs as Drafting Tools, Not Sources of Record

2Decision Rules for AI Drafts Versus Source of Record 3Hallucination Failure Modes and Detection Signals 4Minimum Workpaper Standards for AI-Assisted Outputs 5Verification Primitives for Reperformable Tie-Outs 6Evidence Trails, Data Minimization, and Review Gates

7Prompt Templates as Controlled Accounting Artifacts 8Parameterization and Definitions That Prevent One-Off Prompts 9Structured Output Schemas for QC-Ready Tables and Logs 10Citations-to-Input Rules That Prevent Invented Support 11Uncertainty and Exception Fields That Drive Reviewer Action 12Versioning, Change Logs, and Regression Checks for Prompt Drift

13Intake Triage and Evidence-Ready File Hygiene 14Extraction Schemas for Invoices and Receipts 15Confidence Thresholds and Reviewer Dispositions 16Validation Routines for Extracted Fields 17Exception Queues as a Controlled Workpaper Log 18Policy-Bound Coding Proposals With Privacy Gates and Evidence Links

19Controlled Reconciliations as Workpapers 20Explainable AI Matching Proposals and Verification 21Exception Registers and Break Categorization 22Tie-Outs, Cutoffs, and Reperformance Checks 23Approval Gates and Close Checklist Statusing 24JE Support Narratives and Operational Model Logging

25Evidence-Bound Variance Narratives as a Close Control 26Unsupported Claims Into Questions, Data Pulls, and Follow-Ups 27Reusable Variance Narrative Templates With Exhibits 28Memo and Workpaper Drafting With Alternatives, Risks, and Sign-Off 29Evidence Index Assembly Across Exports, Recons, and Narratives 30Minimal Viable Governance and Tool Evaluation for Controlled Rollout

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AI-Assisted Accounting Workflows With Audit-Ready Artifacts
LLMs as Drafting Tools, Not Sources of Record