Legal

How AI Writes Full Legal Memos in IRAC Format in 2026

The technology helping law students and associates draft structured analysis faster

By Chandler Supple7 min read

Legal memoranda analyze legal questions using structured reasoning. Traditional memo drafting takes 3-5 hours as writers research law, analyze application to facts, and organize findings in IRAC format (Issue, Rule, Application, Conclusion). AI-powered tools now generate complete memo drafts in minutes by asking about the legal issue and jurisdiction. Law students and junior associates use these tools to accelerate research and focus on refining analysis rather than organizing structure.

Why Do Legal Memos Follow IRAC Structure?

IRAC provides logical framework that mirrors legal reasoning. Issue section identifies the legal question. Rule section states applicable law. Application section analyzes how law applies to specific facts. Conclusion section answers the original question. This structure forces complete analysis addressing all necessary elements: identifying the question, understanding governing law, applying law to facts, and reaching reasoned conclusion. Memos using IRAC are easier to follow because readers know where to find each analysis component.

Consistent structure also aids quality control. Supervisors reviewing IRAC memos quickly assess whether analysis is complete by checking that each section addresses required elements. Missing rule citations signal insufficient research. Weak application sections reveal shallow fact analysis. Unsupported conclusions indicate logical gaps. The structure makes analytical weaknesses visible and correctable. This visibility makes IRAC valuable teaching tool for law students learning legal reasoning beyond its utility for practicing attorneys.

According to legal writing research, memos structured with IRAC receive higher ratings from supervising attorneys and judges than unstructured analyses. The format signals analytical rigor and makes arguments easier to follow. Time invested learning IRAC structure pays dividends throughout legal career as the reasoning process becomes intuitive. AI tools that generate IRAC-formatted memos help users internalize the structure through examples.

What Should the Issue Section Include?

Issue sections frame the legal question precisely. They should be specific enough to guide analysis but not assume the answer. "Whether client's non-compete agreement is enforceable in California" is better than "Is the contract valid?" (too vague) or "Why California will not enforce the non-compete" (assumes conclusion). The issue should incorporate key facts affecting the legal question: "Whether non-compete preventing sales work within 50 miles for 2 years is enforceable against employee who signed without legal review in California."

For complex matters involving multiple legal questions, use sub-issues. Main issue: "Whether plaintiff has viable breach of contract claim against defendant." Sub-issues: "Whether valid contract was formed; whether defendant breached material terms; whether plaintiff suffered damages caused by breach." This organization breaks complex analysis into manageable components while showing relationships between questions. Each sub-issue gets its own IRAC analysis within overall memo structure.

  • Frame questions specifically including key facts
  • Use neutral language that does not assume answers
  • Include jurisdiction when law varies by location
  • Break complex questions into sub-issues
  • State issues as whether questions, not yes/no questions

How Should Rule Sections Present Applicable Law?

Rule sections state the governing legal principles with citations. Begin with general rules then address specific elements. "Contracts require offer, acceptance, and consideration. California Civil Code section 1550. Non-compete agreements are generally void in California except for limited exceptions involving business sales. Business & Professions Code section 16600." This progression moves from general contract law to specific non-compete rules relevant to the issue.

Include relevant case law showing how courts have applied rules. "Courts strictly construe section 16600's exceptions. Edwards v. Arthur Andersen held non-compete unenforceable despite substantial consideration because it did not fall within business sale exception. 44 Cal. 4th 937 (2008)." Case citations provide authority and show judicial interpretation. For unsettled questions, present competing views: "Courts are split on whether customer non-solicitation agreements violate section 16600. Compare AMN Healthcare (holding they do) with Loral (suggesting they may not)." This balanced presentation of uncertainty helps readers understand analytical difficulty.

What Makes Application Sections Effective?

Application sections analyze how stated rules apply to specific facts. This is the analysis heart of memos. Address each rule element and explain whether facts satisfy it. "Here, client's agreement prevents sales work within 50 miles for 2 years. This is non-compete within section 16600 because it restricts former employee's ability to work in his profession. Edwards confirms broad interpretation of what constitutes restrained trade. The agreement does not fall within business sale exception because employee did not sell business interest."

Address counterarguments in application section. "Employer may argue agreement is customer non-solicitation, not non-compete, because it only restricts work with existing customers. However, courts in AMN Healthcare rejected this distinction, finding customer restrictions are trade restraints under section 16600." Acknowledging and refuting contrary positions demonstrates thorough analysis and strengthens ultimate conclusion. Ignoring obvious counterarguments makes memos appear incomplete or biased.

How Should Conclusions Be Framed?

Conclusions directly answer the issue question. "The non-compete is likely unenforceable under California law." Use appropriate certainty language: "will," "likely will," "probably," "may," "unlikely," "will not." Legal analysis involves uncertainty. Qualified conclusions acknowledging uncertainty are more accurate than absolute predictions. "The agreement will not be enforced" overstates certainty where case law leaves ambiguity. "The agreement is likely unenforceable based on current precedent" appropriately hedges.

Brief conclusions should reference key reasoning: "The non-compete is likely unenforceable because California Business & Professions Code section 16600 voids such restrictions and the agreement does not fall within recognized exceptions." This quick recap reinforces the analysis. For complex memos with sub-issues, provide mini-conclusions for each before overall conclusion: "Because valid contract was formed (sub-issue 1) and defendant breached material terms (sub-issue 2), plaintiff likely has viable breach claim."

What Additional Memo Components Are Important?

Beyond IRAC analysis, memos typically include heading (to, from, date, re), brief answer, facts section, and sometimes recommendations. Brief answer immediately after heading provides quick conclusion for busy readers who may not read full analysis. "Short Answer: The non-compete is likely unenforceable under California law, which prohibits such restrictions." This 2-3 sentence summary helps readers decide whether to read full analysis or rely on conclusion.

Facts sections summarize relevant facts before IRAC analysis. Include legally significant facts but not unnecessary details. "Employee worked in sales for Company for 3 years. Upon hiring, Employee signed agreement preventing sales work for competing businesses within 50 miles for 2 years after termination. Employee did not have attorney review the agreement." These facts set context for analysis. Recommendations sections suggest actions based on analysis: "We recommend informing employer that agreement is likely unenforceable and proceeding with new employment."

How Should AI Generate Jurisdiction-Specific Analysis?

Legal rules vary by jurisdiction. Non-compete enforceability differs dramatically between California (generally void) and Texas (enforceable if reasonable). AI must ask about jurisdiction and apply appropriate law. Generic analysis using general principles without jurisdiction-specific rules produces inaccurate memos. The AI should identify controlling statutes and leading cases in the specified jurisdiction. For federal questions, note which circuit court has jurisdiction as circuit precedent varies.

For unsettled questions where jurisdiction lacks direct precedent, AI should note this and analyze by analogy to similar cases or other jurisdictions' treatment. "California has not directly addressed this question. However, analogous cases suggest courts would apply X approach. Additionally, majority of other states have adopted Y rule, which California courts may find persuasive." This handles novel questions without overstating certainty. Acknowledging analytical limitations demonstrates intellectual honesty and helps readers assess conclusion reliability.

Use River's legal writing tools to draft and refine legal memos efficiently. AI assistance handles structure, rule research, and initial analysis while you refine reasoning and add nuanced judgment. Better tools mean faster memo drafting and more time for complex analytical work. The result is thorough analysis that supervisors and clients can rely on for legal decision-making.

AI-powered legal memo generation transforms a 3-5 hour drafting task into a 30-45 minute revision process. By asking about legal issues and jurisdiction, AI generates structured first drafts that writers refine for specific facts and strategic considerations. Law students and junior associates benefit from proper IRAC structure, jurisdiction-appropriate rules, and complete analysis framework. The technology handles research and organization while humans provide judgment about fact application and conclusion certainty. This division of labor accelerates memo production while ensuring quality analysis.

Chandler Supple

Co-Founder & CTO at River

Chandler spent years building machine learning systems before realizing the tools he wanted as a writer didn't exist. He founded River to close that gap. In his free time, Chandler loves to read American literature, including Steinbeck and Faulkner.

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