Course Content
Module 1: Introduction to Large Language Models (LLMs) in Law
What LLMs Are (and Aren’t): A Lawyer‑Friendly Mental Model Legal Use Cases & Risk Tiers
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Module 2: Fundamentals of Effective Prompt Design for Legal Tasks
The ICI Framework: Intent + Context + Instruction Advanced Prompt Techniques for Legal Work Prompt Debugging: Lost Middle, Ambiguity, and Token Hygiene
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Module 3: Verifying and Validating AI-Generated Legal Content
Validation Mindset: Why Verification Is Non‑Negotiable Hallucinations in Legal Content: Red Flags & Fixes Bias, Relevance, and Fit: Quality Control Beyond Accuracy
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Module 4: Ethical Considerations and Responsible AI Use in Law
Confidentiality & Data Handling: What You Can Paste Into AI Competence, Supervision, and Accountability with AI Build Your Firm AI Policy Template
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Module 5: Building a Personal Prompt Library and Future Trends
Designing a Personal Prompt Library Future Trends: Specialized Legal Models, RAG, and Agents Build 10 High-Value Prompts You’ll Actually Reuse Final Assessment: Applied Prompt Engineering Scenario
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Prompt Engineering for Legal Applications

Module 2 Knowledge Check (Self‑Check)

This self-check mirrors the Module quiz. Use it to test your understanding. If your site uses Tutor LMS Quiz Import, you can import the CSV quiz file provided in the package instead of (or in addition to) this self-check.

  1. The “ICI” formula for prompt design stands for:

    • Information, Citation, Interpretation
    • Intent, Context, Instruction
    • Input, Content, Inference
    • Identify, Clarify, Implement
  2. When crafting a prompt, providing the jurisdiction (e.g., “under California law”) is an example of including:

    • Intent
    • Instruction
    • Context
    • Reinforcement
  3. Which of the following is an example of assigning a persona in a prompt?

    • “Summarize this document concisely.”
    • “You are a senior litigator specializing in environmental law.”
    • “If the document mentions X, then do Y.”
    • “Provide the output in bullet points.”
  4. True or False: Including too much irrelevant information in a prompt can confuse the LLM and dilute the quality of the output.

    • True
    • False
  5. The “lost middle” bias suggests that LLMs may:

    • Prioritize information in the middle of a prompt.
    • Overlook information in the middle of a long prompt.
    • Struggle with prompts that are too short.
    • Always provide perfectly balanced responses.

Answer key

  1. b
  2. c
  3. b
  4. a
  5. b