TEACHING LEGAL REASONING TO ARTIFICIAL INTELLIGENCE

Common Reasoning Failures in Legal LLMs

Free Guide: Spotting AI Errors in Legal Reasoning

Even purpose-trained legal AI models can make subtle errors, not by hallucinating, but by misapplying precedent, flattening facts, or oversimplifying tests. This free guide outlines the most common reasoning failures in legal LLMs, with real-world signals and mitigation strategies. Use it to strengthen your prompting, review AI outputs like a lawyer, and build trust in AI-assisted legal work.

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COURSE SUMMARY: Teaching Legal Reasoning to AI

Build legal AI prompts that reflect doctrine, not distortion.

The Teaching Legal Reasoning to AI course equips professionals to work responsibly with law-trained AI tools, including purpose-built legal LLMs and AI systems used for legal research, drafting, and review. It focuses on doctrinally accurate, jurisdiction-aware prompt design and response evaluation.

Whether you're overseeing AI outputs in a legal department or shaping how models are used in regulated workflows, this course helps ensure AI assistance doesn’t compromise legal reasoning.

You’ll learn how to:

Apply core legal doctrines (like stare decisis, Glucksberg, and Chevron) in prompt structures that support correct legal analysis
Identify and correct AI-generated reasoning errors, such as misapplied precedent or jurisdictional overreach
Draft clear, test-driven legal prompts that align with professional ethics and reduce risk
Use structured legal reasoning methods (IRAC, CREAC, precedent hierarchy) to guide and evaluate outputs

Every module includes practical exercises, case-based prompt breakdowns, and structured review tools, so you’re not just using legal AI. You’re ensuring it thinks like a lawyer, not just sounds like one.

TEACHING LEGAL REASONING TO aI

PRIMER: WHY LEGAL REASONING MATTERS

Learning Outcome: Understand how case-based reasoning, precedent, and doctrinal logic form the foundation of legal method, and why these traditions are essential when using or evaluating AI systems trained for legal tasks.

0.1 What Is Legal Reasoning?

Logic, precedent, and structure, not opinion
Why "thinking like a lawyer" is different from summarizing law
The IRAC / CREAC structure, in plain language

0.2 Why Even Legal LLMs Get Law Wrong

Pattern-matching ≠ doctrinal understanding
Common failures: skipping scrutiny steps, mislabeling tests
Why structured prompts reduce risk and raise trustworthiness

0.3 What Makes a Legal Output Trustworthy?

Doctrinal structure > persuasive tone
Precedent hierarchy, jurisdiction, and citations
Why "sounds correct" can still be legally flawed

0.4 Legal Reasoning in the Common Law Tradition

Precedent and the doctrine of stare decisis
Binding vs. persuasive authority, vertical vs. horizontal precedent
Why legal LLMs often misread dicta or misapply analogies
Ratio decidendi vs. obiter dicta: teaching models to tell the difference
Structuring AI prompts to reflect how courts reason
Fact vs. law, mixed questions, and appellate logic

MODULE 1: CORE LEGAL TESTS THAT LEGAL AI MUST GET RIGHT

Learning Outcome: Learn to recognize foundational legal doctrines (e.g., strict scrutiny, Chevron, Glucksberg) and understand how legal AI models can misapply or flatten them, so you can correct and supervise AI outputs responsibly.

1.1 How Legal LLMs Misapply The “Big Four”

Strict Scrutiny, Intermediate Scrutiny, Rational Basis
Doctrinal gaps: missing burdens, skipping tailoring
Why legal models sometimes label the test without applying it
Glucksberg Test for Substantive Due Process
The “deeply rooted” prong and why legal LLMs struggle with historical framing
Clarifying that rights ≠ values

1.2 The Chevron Framework: Where Legal LLMs Still Slip

Step 1: Statutory ambiguity

- Shortcutting to plain meaning

Step 2: Reasonableness of agency interpretation

- Confusion between Chevron and Skidmore

Prompt designs that force structured analysis of ambiguity and deference

1.3 Stare Decisis Logic: Beyond Citation

Casey’s stare decisis factors: workability, reliance, doctrinal development, and factual change
Why legal LLMs over-rely on frequency of citation
Teaching models to weigh precedent — not just repeat it

MODULE 2: BUILDING BETTER LEGAL PROMPTS

Learning Outcome: Develop structured, test-aligned prompts that guide AI toward accurate legal reasoning by applying legal method frameworks, prompt anatomy, and doctrinal awareness across various use cases.

2.1 “Apply the Test” Prompts

Step-by-step prompting for IRAC-style outputs
Break down complex legal questions into explicit doctrinal steps
How to prevent skipped prongs (e.g., compelling interest with no narrow tailoring)
Prompt structures that force the model to reason, not just regurgitate
Using nested prompts to mirror how lawyers argue legal issues

2.2 “Compare Majority vs. Dissent” Prompts

How to force deeper analysis
Teach AI to weigh arguments, not just summarize holdings
Techniques to draw out reasoning from both sides
Using prompts to highlight: logic gaps, alternate doctrines, and judicial values
Avoiding “verdict bias” in single-outcome responses

2.3 “Cite and Apply Precedent” Prompts

How to guide LLMs to:

- Choose the right jurisdiction

- Identify controlling vs. persuasive authority
- Apply holdings, not just quote headnotes

Preventing fake case law or misused precedent
Prompt scaffolds that require the AI to “show its legal work”

MODULE 3: TROUBLESHOOTING BAD OUTPUTS

Learning Outcome: Identify common legal reasoning failures in AI responses (e.g., wrong test, missing steps, jurisdictional drift) and learn how to spot, critique, and revise them using diagnostic techniques and prompt engineering strategies.

3.1 Spotting Core Reasoning Failures

Recognizing when a case is cited but not applied
Detecting use of the wrong legal standard (e.g., rational basis when strict scrutiny applies)
Identifying jurisdictional mismatches (e.g., U.S. test cited in a U.K. context)
Noting failures of logic, completeness, or legal structure

3.2 Correcting “Fuzzy” Outputs with Targeted Prompting

Turning vague prompts into IRAC-aligned legal questions
Isolating and testing the part of the prompt where the LLM goes astray
Using scaffolded re-prompts to eliminate hallucinations and fill doctrinal gaps
Engineering prompts that require application, not just summarization

Ready to train your legal AI to reason, not just recite?

Individual and team pricing available upon request.
Teaching Legal Reasoning to AI is priced to reflect its depth, professional relevance, and the real-world risks it helps you manage.
Contact us for a quote.