Prepare a hearing prep sheet with likely judge questions
Condenses a motion or hearing into a one-page argument map, the toughest likely questions, and short answers.
The prompt
Help me prepare for a court hearing or oral argument.
Hearing type and what is being decided: [e.g. "motion to dismiss", "preliminary injunction", "case management conference"]
Our position and the relief we want: [SHORT]
Our main arguments: [LIST or PASTE brief summary]
Other side's main arguments: [LIST or PASTE]
Key authorities each side relies on: [names and citations as they appear in the briefs]
Key facts and record cites: [LIST]
Time allotted: [MINUTES]
What I know about the judge's approach: [OPTIONAL]
Return:
1. Opening (60-90 seconds) - what we want and the 2-3 reasons the court should grant it.
2. Argument map - each point with the one fact and one authority that best supports it.
3. Hardest questions - 10 questions the judge is likely to ask, especially on our weak points, each with a 2-3 sentence answer and the record or brief cite to point to.
4. Concessions - what we can safely concede and what we must never concede.
5. Rebuttal points for each of the other side's strongest arguments.
6. Fallback ask if the court will not give us everything (e.g. narrower relief, leave to amend).
7. One-page cheat sheet of key cites, dates, and record pages.
Use only authorities I have given you; never add or describe case law from memory. Flag any authority whose holding I should re-check with [VERIFY]. A qualified lawyer must confirm all citations and positions before the hearing.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Legal & Compliance prompts
What to fill in
- SHORT
- LIST or PASTE brief summary
- LIST or PASTE
- names and citations as they appear in the briefs
- LIST
- MINUTES
- OPTIONAL
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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