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

Welcome & How to Use This Course

You’ll get the most value from this course if you treat it as a practice lab—not a lecture. You will write prompts, test outputs, and refine your process the same way you’d refine a brief draft.

Key takeaways

  • Prompting is iterative: draft → test → refine.
  • Verification is part of the workflow, not an optional step.
  • You remain responsible for the final work product.

Suggested workflow

  1. Watch/read the lesson.
  2. Practice the prompts using a tool your organization approves.
  3. Compare outputs across 2–3 prompt variations.
  4. Validate any legal facts/citations using authoritative sources.
  5. Save your best prompt version to your prompt library.

What this course is (and is not)

  • This is not legal advice.
  • This is not a guarantee that AI outputs will be correct.
  • This is a practical framework for using AI tools responsibly in legal workflows.

Your professional responsibility

AI tools can help you draft and analyze, but they do not remove your duty of competence, confidentiality, and verification. Treat AI output like work from a junior team member: helpful, fast, and sometimes wrong.