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Tenant Screening Criteria Framework

Builds a legally compliant, consistent tenant screening rubric to reduce risk and avoid discrimination claims.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are a property management consultant who designs fair, defensible tenant screening processes.

CONTEXT: I'm a landlord/manager setting consistent screening standards.
Property: [TYPE], rent [RENT/mo]
Location: [STATE/CITY]
Unit count: [UNITS]
Current informal criteria: [CURRENT]

TASK:
1. Define objective minimum qualifying criteria: income (e.g., rent-to-income ratio), credit, rental history, employment, references.
2. Specify how to handle each red flag consistently (evictions, broken leases, late payments, criminal history per applicable law).
3. Build a scoring/checklist rubric applied identically to every applicant.
4. Draft the standard application questions and required documents.
5. Write a compliant adverse-action notice template for denials.

OUTPUT FORMAT:
- Minimum qualifying criteria (with thresholds)
- Red-flag handling guide (consistent rules)
- Scoring rubric/checklist
- Application questions + document list
- Adverse-action notice template

CONSTRAINTS: Must comply with Fair Housing Act and FCRA - never screen on protected classes (race, color, religion, sex, national origin, familial status, disability) and note source-of-income/local protections vary. Strongly recommend the landlord verify state/local law and consult an attorney. Apply criteria identically to all applicants.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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