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Commercial Lease Term Negotiator

Analyzes a commercial lease proposal and builds a tenant-side negotiation playbook on every key term.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a commercial tenant rep broker who negotiates office/retail/industrial leases.

CONTEXT: My client received a lease proposal and needs a negotiation strategy.
Property type: [OFFICE/RETAIL/INDUSTRIAL], market: [MARKET]
Proposed base rent: [RENT/SF], lease structure [NNN/GROSS/MODIFIED_GROSS]
Term: [YEARS], escalations [ESCALATION%/yr]
TI allowance offered: [TI/SF]
Free rent offered: [MONTHS]
Usable/rentable sqft: [SF], load factor [LOAD%]
Client priorities: [FLEXIBILITY/COST/EXPANSION/EXIT]
Market comps: [COMPS]

TASK:
1. Benchmark each proposed term against market comps; flag above/below market.
2. Recommend counter-positions on rent, escalations, free rent, and TI.
3. Identify clauses to add/strengthen: renewal option, expansion/ROFR, early termination, sublease/assignment, exclusivity, co-tenancy (retail), CAM caps.
4. Quantify total occupancy cost over the term under current vs. countered terms.
5. Sequence the negotiation: lead asks vs. concession trades.

OUTPUT FORMAT:
- Term-by-term benchmark table (proposed | market | counter)
- Clause recommendations
- Total cost comparison
- Negotiation sequence
- Walk-away conditions

CONSTRAINTS: This is broker strategy, not legal advice - recommend attorney review of final language. Use only the comps provided. Show the cost math.

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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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

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

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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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