Build a copy-paste prompt pack for me as a business analyst working on [PROJECT OR DOMAIN] for [STAKEHOLDERS]. My available source material is [SOURCE MATERIAL], and my current tools and limits are [TOOLS AND CONSTRAINTS]. Create 10 prompts I can use in ChatGPT, Claude, or Copilot. Each prompt must be written in first person as if I am sending it, contain its own bracketed inputs, and be 70-120 words. Cover these day-to-day tasks: 1. Turn stakeholder notes into testable business requirements and acceptance criteria. 2. Identify ambiguities, conflicts, and missing decisions in a requirement. 3. Map an as-is and to-be process, including handoffs, exceptions, controls, and systems. 4. Build a stakeholder interview guide that distinguishes facts, preferences, and constraints. 5. Convert a business problem into measurable KPIs, definitions, owners, and data caveats. 6. Reconcile two reports with conflicting numbers. 7. Draft user stories with nonfunctional requirements and edge cases. 8. Create a UAT test scenario matrix. 9. Write a concise decision log entry. 10. Summarize a working session with owners, dates, dependencies, and unresolved items. For every prompt, specify the exact output format it should request. Require the AI to cite which supplied note or data point supports each conclusion and flag rather than resolve unsupported assumptions. Include up to three clarifying questions only when inputs required for that individual task are absent. Start with a one-paragraph guide explaining when an analyst should use AI for structuring versus when they must validate with the process owner, data owner, security, legal, or compliance team. Do not fabricate system behavior, policies, data definitions, or stakeholder agreement. Before answering, check that the 10 prompts are materially different and that each produces an analyst artifact, not generic advice.
Fill in
| Placeholder | What to enter | Example |
|---|---|---|
| [PROJECT OR DOMAIN] | Name the project, product area, or operational domain you analyze. | Claims-intake workflow redesign for a regional insurance carrier. |
| [STAKEHOLDERS] | List the business, technical, and decision-making stakeholders. | Claims operations director, adjuster leads, Salesforce admin, data engineering, and compliance counsel. |
| [SOURCE MATERIAL] | Describe the notes, reports, process documents, tickets, or data available. | Six discovery-call transcripts, current SOP, Jira backlog, weekly claims-aging report, and 20 sample cases. |
| [TOOLS AND CONSTRAINTS] | State the systems, data access, delivery method, and policy constraints. | SQL read access, Power BI, Jira, and no production data in public AI tools. |
How to use
- Replace the four context fields with the project details that recur across your work.
- Save the resulting prompts in your team’s approved workspace and adapt their bracketed inputs per assignment.
- Check outputs against the source note, report, or process owner before treating them as requirements or facts.
- Send this follow-up: “Add a prompt for identifying data lineage and metric-definition conflicts in this project.”
Variations
Requirements workshop
Use this before a requirements workshop with multiple stakeholders.
Create a workshop kit for [INITIATIVE] with [ATTENDEES] using [PRE-READ MATERIAL]. Produce a 60-minute agenda, neutral questions, a decision register, parking-lot categories, and a requirements capture template. For each question, state what decision or ambiguity it resolves. Include prompts that uncover exceptions, approval rules, data ownership, service levels, and audit needs. Do not infer agreement from silence or turn a preference into a requirement. Check that every agenda item has an intended artifact or decision. Ask up to three questions only if a required workshop detail is missing.
Data reconciliation
Use this when two dashboards or reports disagree.
Help me reconcile [REPORT A] and [REPORT B] for [METRIC OR BUSINESS QUESTION]. I can provide [DEFINITIONS, QUERIES, AND SAMPLE ROWS]. Produce a comparison plan with a data-dictionary table, likely causes of variance, ordered validation tests, and a finding log template. Test scope, time zone, grain, filters, joins, exclusions, refresh timing, and null treatment before assuming a calculation error. Do not declare either report correct without evidence. Check that each proposed test can distinguish between at least two plausible causes. Ask up to three questions only for missing inputs.
UAT scenarios
Use this after requirements are stable enough to test.
Create a UAT scenario matrix for [FEATURE OR PROCESS] from [REQUIREMENTS AND RULES], for [USER ROLES]. Return a table with scenario ID, preconditions, steps, expected result, test data needed, priority, owner, and evidence to capture. Cover happy paths, permissions, invalid inputs, duplicate handling, integration failures, notifications, reporting effects, and recovery from interruption. Mark requirements that cannot be tested from the supplied material. Do not invent business rules. Check that each acceptance criterion maps to at least one scenario and each high-risk exception has a test. Ask up to three questions only if necessary.
Tips
- Ask the model to separate a business requirement from a proposed solution; “the system must” often hides a design choice that stakeholders have not approved.
- For metric work, define grain, population, time window, exclusions, and refresh cadence before comparing values across reports.
- Treat meeting summaries as drafts until named owners confirm decisions and due dates; AI can make implied commitments sound more definite than they were.
- Keep an assumption log beside requirements because an assumption that survives into UAT often becomes an expensive defect.
FAQ
Can an AI tool write user stories from workshop notes?
Yes, but it should preserve source references and label unanswered questions. A product owner and affected users still need to validate the story and acceptance criteria.
What should I remove before pasting analyst material into AI?
Remove customer identifiers, credentials, protected data, and any information barred by your organization’s AI policy. Use an approved enterprise tool when sensitive material is necessary.
Can AI reconcile conflicting reports?
It can create a rigorous test plan and inspect supplied definitions or samples. It cannot establish the truth without access to the underlying data and the owners of both reports.