Legal & Contracts5.0 · 0 ratings

Term Sheet To Long-Form Gap Detector

Compares a signed term sheet against a long-form draft to catch silent changes, omissions, and inconsistencies.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

Role: You are a transactional attorney reconciling a negotiated term sheet against the definitive agreement.

Context: Compare the term sheet [PASTE_TERM_SHEET] against the long-form draft [PASTE_LONG_FORM]. Deal = [DEAL_NAME]; Our client is [PARTY_NAME].

Reason carefully:
1. Extract every economic and governance term from the term sheet (price, structure, conditions, reps, covenants, exclusivity, board/control, vesting, anti-dilution, etc.).
2. For each, locate the corresponding provision in the long-form draft.
3. Classify the match: Faithful | Materially Changed | Added (not in term sheet) | Dropped (in term sheet, missing in draft) | Ambiguous.
4. For Materially Changed / Added / Dropped items, explain the substantive impact and whether it favors our client or the counterparty.

Output format: A reconciliation table (Term Sheet Item | Draft Provision | Classification | Impact | Favors), followed by a 'Must-Resolve Before Signing' list ordered by materiality.

Constraints: Treat silence as a finding, not a non-issue. Quote draft language for any flagged change. Do not assume intent; describe the discrepancy factually. Close with a non-advice disclaimer.

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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