Client Deduction Interview Script Generator (Transactions + Prior-Year → Smart Q&A → Evidence Request Pack)
Quickly produce a tailored deduction interview and evidence request list that cuts email ping-pong and drives complete, accurate client responses.
Prompt Overview
Tips For You
Start with high-dollar vendors to personalize the first five questions. | Send the evidence checklist as a separate, bolded list to improve response rates. | Use conservative language where client records are weak. | Track follow-ups in a simple two-column Excel sheet (item, status).
From Operations TeamNexusAi TechnologyProblem It Solves
Unstructured client questioning leads to missed deductions and rework. This prompt creates a branching interview and clear request list aligned to tax rules.
Branching interview logic
Dynamically adapts based on client answers to surface hidden deductions.
Evidence-first design
Pairs every question with precise substantiation requests.
Risk triage
Flags gray areas and proposes safer alternatives.
Workpaper notes
Generates concise preparer notes ready for your files.
AI Prompt Instructions
Act as: A senior tax preparer and audit-ready documentation specialist for small business and sole proprietor returns.
Why this task matters: Interviews are often ad-hoc, causing incomplete answers, missed deductions, and weak substantiation. A structured AI-driven script increases deduction capture while enforcing compliance.
Important boundaries:
- Do not provide legal advice; focus on tax documentation best practices and generally accepted substantiation.
- Assume U.S. federal rules unless the user specifies a different jurisdiction.
- Prefer conservative, evidence-backed positions; flag gray areas.
User inputs (paste or summarize):
- Business profile (entity type, industry, revenue range)
- Prior-year deductions taken and notes
- Current-year transaction highlights (top vendors, unusual large items)
- Known deduction areas: vehicle, home office, travel, meals, supplies, contractors, depreciation
Objectives:
1) Generate a branching interview script with smart follow-ups.
2) Produce a precise evidence request list (documents, logs, screenshots) per deduction area.
3) Identify risky or gray areas and propose safer alternatives or required disclosures.
4) Create preparer notes and a summary to paste into workpapers.
Analysis workflow:
1) Segment inputs by deduction area and typical substantiation (e.g., mileage log, receipts, business purpose).
2) For each area, design primary questions, conditional follow-ups, and red-flag probes.
3) Map each question to evidence requirements and acceptable substitutes.
4) Assign a risk level (Low/Medium/High) with reasoning and recommended next step.
5) Compile a concise notes section for preparer workpapers.
Required output format:
- Section A: Interview Overview (bullets)
- Section B: Branching Questions by Area (numbered with if/then follow-ups)
- Section C: Evidence Request Checklist (by area, with file examples)
- Section D: Risk Flags & Guidance (table: area | issue | risk | action)
- Section E: Preparer Notes (bullet summary for workpapers)
Quality controls:
- Avoid overly broad questions; be specific and reference evidence.
- Ensure each risky answer triggers at least one follow-up.
- Keep total interview length manageable; prioritize high-value areas first.
Verification checklist:
- Do questions cover vehicle, travel, meals, home office, supplies, contractors, depreciation if relevant?
- Are evidence items precise (e.g., mileage log fields, invoice + proof of payment)?
- Are instructions free of legal advice?
Final instruction: Produce the full output in the specified sections, ready to paste into ChatGPT/Claude/Gemini or a document, with clear labels and bullet formatting. Do not request additional info unless critical; use placeholders like [CLIENT NAME], [TAX YEAR], and [ENTITY TYPE] where needed.
Expected Outcome
Section A: Interview Overview: Focus on vehicle, meals, home office. Section B: Branching Questions: 1) Vehicle: If business use claimed, ask for mileage start/end, total miles, log method; If no log, ask for calendar evidence. Section C: Evidence Checklist: Mileage log with date, purpose, odometer; fuel/maintenance receipts. Section D: Risk Flags: Vehicle | No mileage log | High | Propose standard mileage method. Section E: Preparer Notes: Client likely eligible for standard mileage; request contemporaneous log.
Implementation Journey
Generate the interview in ChatGPT
Open ChatGPT. Paste business profile, prior-year deductions, and 5–10 notable vendors. Paste the prompt. Request a branching interview, evidence checklist, and preparer notes. Expect a structured, labeled script and checklists.
10 minutesOrganize questions and evidence in Excel
Copy the interview into Excel with columns: Area, Question, Evidence, Risk, Status. Use filters to identify high-risk items and create a short list of must-have documents for the first client message.
15 minutesSend client-ready request pack
Paste the Evidence Request Checklist into your client portal or email. Clearly label placeholders like [CLIENT NAME] and [TAX YEAR]. Track replies in Excel and feed new info back into ChatGPT to refine follow-ups.
10 minutes