Legal AI prompts need precise roles and situations. Why? Because audience determines structure. Consider the example of a simple deposition summary. An internal summary serves your team's needs. A client version serves different ones. Client experience also matters: those with legal departments need different summaries than first-time litigants. Instructing AI to "be a senior associate" and "summarize this deposition" can work, but the output will likely fall short of what you might actually need.

💡
If you are unsure what specific context is required in your situation, you can ask the AI what context and additional information it would need to provide a better response.

Take a look below at some tips to help you better contextualize prompts for litigation purposes.

1. Specify the AI's Professional Identity

  • Define the exact legal role (e.g., "senior litigation associate," "legal analyst," "document review specialist")
  • Include relevant jurisdictional expertise when applicable
  • Mention years of hypothetical experience or expertise level

Why this matters: Different legal roles require different analytical frameworks and communication styles. A "senior litigation associate" will provide more strategic analysis and consider broader case implications, while a "document review specialist" will focus on granular detail and systematic organization. Specifying experience level helps calibrate the sophistication and depth of analysis.

2. Establish the Matter Context

  • Type of litigation (commercial, employment, securities, etc.)
  • Court/forum (federal, state, arbitration)
  • Stage of litigation (discovery, motion practice, trial prep)
  • Party representation (plaintiff/defendant, position in multi-party litigation)

Why this matters: Legal standards, procedural rules, and strategic considerations vary significantly across practice areas and forums. An AI analyzing documents for a securities fraud case will look for different red flags than one reviewing employment discrimination materials. The litigation stage affects what's most relevant—discovery-phase analysis prioritizes different issues than trial preparation.

3. Define Working Parameters

  • Tone and audience (formal brief vs. internal memo vs. client communication)
  • Level of detail required (high-level summary vs. granular analysis)
  • Time sensitivity or deadlines
  • Any specific legal standards or frameworks to apply

Why this matters: This prevents the AI from providing a detailed legal memorandum when you need a quick client email, or vice versa. Mentioning time sensitivity can help the AI prioritize the most critical issues rather than attempting exhaustive analysis. Identifying specific legal frameworks ensures the AI applies the correct analytical approach rather than generic legal reasoning.

4. Provide Case-Specific Background

  • Key parties and their relationships
  • Central legal claims and defenses
  • Relevant facts already established
  • Critical documents or testimony already in record

Why this matters: Without case context, the AI may miss crucial connections or provide generic analysis that ignores case-specific nuances. For example, knowing that a witness is the plaintiff's former business partner versus a neutral third party completely changes how their testimony should be evaluated. This background helps the AI identify what's significant versus routine.

💡
Often the easiest way to provide case-specific context is to upload documents from your case and instruct the AI to review, analyze, and consider those documents as part of its task. As always with attaching docs - I'd be careful with confidential information if you are not using a private, firm-approved AI platform.

5. Clarify Output Expectations

  • Format requirements (bullet points, narrative, formal structure)
  • Length parameters
  • Key issues to prioritize
  • Any specific questions that must be addressed

Why this matters: Clear formatting instructions prevent time-consuming reformatting. Length parameters ensure you get usable work product—not a 10-page analysis when you only need a few bullet points. Prioritizing issues ensures the AI doesn't bury critical findings in less important details.

6. Include Relevant Constraints and Sensitivities

  • Privilege concerns or confidentiality parameters
  • Political sensitivities or public relations considerations
  • Settlement posture or litigation strategy
  • Budget or resource limitations

Why this matters: Legal work doesn't happen in a vacuum. Knowing that your client is seeking settlement versus taking an aggressive litigation stance will affect how the AI frames its analysis. Understanding PR sensitivities helps avoid suggestions that might be legally sound but practically problematic.

7. Specify Known Pitfalls or Areas of Concern

  • Weak points in your case
  • Particularly credible opposing witnesses
  • Unfavorable precedents in your jurisdiction
  • Documents or facts you know are problematic

Why this matters: By acknowledging weaknesses upfront, you get more realistic and useful analysis. The AI can help you develop counter-arguments or mitigation strategies rather than providing overly optimistic assessments that ignore known problems. This leads to more sophisticated work product that anticipates challenges.

8. Define Technical or Industry-Specific Context

  • Industry terminology or standards
  • Technical processes relevant to the dispute
  • Regulatory framework or compliance requirements
  • Standard business practices in the relevant field

Why this matters: Many commercial disputes involve industry-specific knowledge. An AI analyzing a construction defect case needs different context than one reviewing a pharmaceutical patent dispute. Providing this context upfront ensures the AI doesn't misinterpret technical terms or miss industry-standard practices that could be crucial to liability or damages arguments.