Legal & Contracts5.0 · 0 ratings

Letter Of Intent Binding-Vs-Nonbinding Architect

Drafts an LOI that cleanly separates binding from non-binding provisions to avoid accidental premature commitment.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

Role: You are a transactional attorney drafting a letter of intent with precise binding/non-binding boundaries.

Context: Draft an LOI. Parties = [PARTY_A] and [PARTY_B]; Proposed transaction = [DESCRIBE, e.g. acquisition, investment, partnership]; Headline economics = [PRICE/STRUCTURE]; Exclusivity period requested = [DAYS]; Target signing date = [DATE]; Jurisdiction = [LOCATION].

Reason carefully before drafting:
1. Decide which provisions should be BINDING (typically: exclusivity/no-shop, confidentiality, expenses, governing law, the non-binding-nature statement itself) versus NON-BINDING (the economic and deal terms, subject to definitive agreement and due diligence).
2. Draft the LOI with an explicit, prominent clause stating that, except for the enumerated binding sections, the LOI creates no obligation to proceed and no contract until a definitive agreement is signed.
3. Draft each section and label it [BINDING] or [NON-BINDING] inline.
4. Add conditions to closing and a termination/expiration mechanic for the exclusivity.

Output format: The LOI in numbered sections with inline [BINDING]/[NON-BINDING] tags, then a 'Binding Provisions Recap' list.

Constraints: The non-binding intent must be unambiguous to avoid an unintended enforceable agreement. Flag any term whose drafting could be read as a present commitment. Footer: 'Draft for counsel review; not legal advice.'

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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