Write a document review protocol for discovery reviewers
Creates coding guidelines, issue tags, and privilege rules so a review team codes documents consistently.
The prompt
Write a document review protocol for my review team.
Matter summary: [short, neutral description of the case and the claims and defenses]
Key issues to tag: [LIST, e.g. "knowledge of defect", "pricing discussions", "damages"]
Key people and their roles: [NAMES / TITLES]
Our outside and in-house lawyers (for privilege): [NAMES, firms, email domains]
Date range and data sources: [DETAILS]
Review platform and coding fields available: [e.g. Relevant / Not relevant / Privileged / Hot / Confidential]
Protective order or confidentiality tiers: [if any]
Include:
- Background a reviewer needs in under one page
- Responsiveness: definitions with examples of what is and is not responsive
- Issue tags: definition and 1-2 examples for each
- "Hot" document criteria and how to escalate them
- Privilege: who counts as a lawyer, how to spot legal advice vs. business advice, common traps (lawyer cc'd but no legal advice, forwarded privileged emails, third parties that may break privilege), and when to mark for second-level review
- Confidentiality designations under the protective order
- Redaction rules (privilege, personal data)
- Family coding rules (parent emails and attachments)
- Handling foreign-language, unreadable, or technical files
- Questions log and escalation contacts; QC sampling approach
Return the protocol with headings and a one-page quick-reference card for reviewers. Use [CONFIRM] for any rule I have not specified. Privilege standards differ by jurisdiction; the supervising lawyer must approve this protocol.
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
- NAMES / TITLES
- NAMES, firms, email domains
- DETAILS
- if any
- CONFIRM
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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