June 14, 2025

Navigating AI in Legal Practice: Essential Parameters for Lawyers

The legal profession, traditionally a bastion of precedent and measured change, is now facing a technological wave unlike any before it: Artificial Intelligence. Tools powered by Large Language Models (LLMs) are quickly becoming accessible, promising unprecedented efficiency in tasks ranging from document review to initial legal research. For many law firms, this signals an opportunity to significantly reduce billable hours for certain tasks, translating directly into valuable cost savings for clients.

Indeed, while legal practitioners have historically been considered "slow adopters" of new technology, ignoring the rapid advancements in AI is no longer a viable option. Firms that fail to leverage these tools responsibly risk being outpaced in efficiency and cost-effectiveness, potentially disadvantaging both their clients and their own practice.

However, the rise of AI in law comes with significant perils. Publicized cases of AI "hallucinations"—where the technology invents non-existent legal citations or misstates facts—have led to professional embarrassment, sanctions, and even threats to lawyers' licenses. Some quick examples: Mashable, Reuters,  AI Hallucinations Cases. And it's not just lawyers, as shown by the recent allegation that even the Federal Government may have relied on AI Hallucinations in a recent HHS report. 

This article aims to provide a foundational understanding of AI for lawyers just beginning to explore its use. It focuses on the basic functionality and inherent limitations of these tools, particularly the risk of "hallucinations." Please note: the scope of this article is geared towards the lawyer exploring the fundamental principles of AI integration and is not intended to explore the complex capabilities of the rapidly changing, law-focused AI marketplace or the bespoke in-house setups some larger firms are investing in. Adopting AI for complex tasks requires rigorous oversight and a disciplined approach, especially in high-stakes legal work.

Understanding AI Models: How They Work (and Don't Work) in Legal Contexts

At their core, Large Language Models (LLMs) (like ChatGPT, Google Gemini, Meta AI, and others) are sophisticated computer programs trained on massive datasets of text. Think of them as incredibly advanced prediction engines: they analyze patterns in the words they've "read" and then predict the next most probable word or phrase in a sequence. This allows them to generate human-like text, summarize documents, translate languages, and answer questions.

Crucially, LLMs do not "understand" the law in a human sense. They don't possess legal judgment, ethical reasoning, or a true grasp of legal precedent. While they can process and recognize patterns within legal documents they were trained on, their function is to generate coherent text, not necessarily to provide factually accurate or legally sound information based on genuine understanding. They are pattern-matching machines, not truth-tellers.

The "Hallucination" Phenomenon: What It Is and Why It Happens

The most alarming limitation of current LLMs, particularly for legal professionals, is the phenomenon known as "hallucination." An AI "hallucination" refers to the generation of plausible-sounding but entirely false or nonsensical information. In a legal context, this can include fabricating non-existent case citations, statutes, legal principles, or even factual scenarios.

Why do these hallucinations occur?

• Lack of "Knowledge": LLMs don't "know" facts in the way humans do. They infer relationships from their training data. When asked for something outside their dataset, or when a request is ambiguous, they may "make up" information to fulfill the request for coherent text.

• Confidence vs. Accuracy: AI models present their generated content with a high degree of confidence, regardless of its factual basis. They don't "know" they're wrong.

• Outdated Training Data: General LLMs often have a knowledge cutoff date. They will not reflect the most recent legal developments, new cases, or legislative changes, leading to outdated or incorrect information.

• Subtle Nuance: Legal language is highly nuanced and context-dependent. LLMs can miss critical distinctions or misinterpret subtle contextual cues, leading to inaccurate outputs.

• Prompt Ambiguity: Vague or poorly structured prompts can steer the AI down an incorrect path, prompting it to invent information to fill perceived gaps.

Essential Parameters for Lawyers Using AI in Legal Research & Writing

The overarching principle for any lawyer considering AI integration is clear: AI is a tool to assist, not a substitute for professional judgment and diligent human verification.

1. Duty of Competence (Texas Disciplinary Rule 1.01 Comment 8):
   Lawyers have an ethical obligation to provide competent representation. This includes understanding the technology utilized in practice, recognizing both its benefits and its significant risks. The Texas Disciplinary Rules of Professional Conduct, specifically Comment 8 to Rule 1.01 (Competent and Diligent Representation), states that lawyers "should strive to become and remain proficient and competent in the practice of law," including understanding "the benefits and risks associated with relevant technology." This explicitly makes technological competence an ethical duty in Texas. A lawyer cannot delegate professional responsibility or judgment to AI.

2. Privacy and Confidentiality Risks (Texas Disciplinary Rule 1.05):
   A fundamental and absolute ethical duty of every lawyer is to protect client confidentiality and sensitive information. Breaching this duty can lead to severe professional consequences. AI introduces new vectors for these risks:

   • Public AI Models: Never input confidential, privileged, or sensitive client information into general-purpose, public AI models (e.g., free versions of widely accessible chatbots). These models often use your input for training data, meaning any sensitive client details (case facts, client names, proprietary business information, etc.) could potentially be learned by the AI and later surfaced to other users, or become accessible to the AI provider. There is no attorney-client privilege protecting information shared with these tools.

   • Cloud-Based Legal AI Tools: Even specialized legal AI tools operating in the cloud carry risks. While often more secure, a lawyer must rigorously scrutinize their terms of service, privacy policies, and data security protocols. Key questions to ask include: Does the provider promise not to use your data for training? Is the data encrypted in transit and at rest? What are their data retention policies? Are they compliant with relevant data protection regulations?

   • Best Practices: Always prioritize AI solutions specifically designed for legal professionals that guarantee data privacy, do not use client data for training, and comply with ethical obligations. If using any AI tool for drafting or analysis of case facts, strictly anonymize and redact all identifying information.

3. Verification is Paramount (The Golden Rule):
   Every single piece of information generated by AI, especially case citations, statutory references, factual assertions, or legal summaries, MUST be independently verified. This requires consulting traditional, authoritative legal research databases (e.g., Westlaw, LexisNexis, Fastcase) and reviewing the original source documents. Do not rely on the AI's assertion that a source exists or says what it claims. Specifically check:

   • Does the case, statute, or rule actually exist?

   • Does it stand for the legal proposition cited?

   • Is it still good law (not overruled, repealed, or superseded)?

   • Are the citations accurate (parallel citations, proper court, date, etc.)?

4. Smart Prompt Engineering:
   The quality of AI output is highly dependent on the quality of the input.

   • Be Specific and Clear: The more precise your prompt, the better the output. Avoid ambiguity.

   • Provide Context: Give the AI enough background information to understand the scope of your request.

   • Define Output Requirements: Specify format, length, tone, and what elements you want included (e.g., "Summarize Roe v. Wade, focusing on the trimester framework, and provide pinpoint citations to the holding for each trimester.").

   • Iterate and Refine: If the initial output isn't satisfactory, refine your prompt. Break down complex tasks into smaller, manageable requests.

   • Ask for Sources: Always include a request for citations and source material, but remember these must be independently verified.

5. Human Oversight and Judgment:
   AI tools should augment, not replace, a lawyer's critical thinking, legal analysis, and strategic judgment. The final work product must always reflect the lawyer's professional judgment and understanding of the law. Review all AI-generated content for accuracy, completeness, relevance, and persuasive power.

6. Transparency and Disclosure:
   Consider discussing the use of AI with clients, especially if it leads to significant efficiency gains. Explain how it benefits efficiency and how risks are mitigated, framing it as a way to potentially save them money while maintaining quality. In some jurisdictions, judges are beginning to require disclosure of AI assistance in court filings. Even without a formal rule, candor to the tribunal might necessitate disclosure if AI generated substantive legal arguments or research. Always be mindful of local rules and judge-specific requirements.

7. Firm Policies and Training:
   Law firms should establish clear, written policies on the permissible and prohibited uses of AI. Provide ongoing training for all legal professionals (attorneys, paralegals, staff) on AI tools, best practices, ethical considerations, and the firm's specific AI policies. Implement internal review processes for AI-assisted work.

8. Billing Considerations:
   If AI makes a task more efficient, hourly billing should reflect the actual time spent, not the time it would have taken without AI. For flat fees, consider whether the increased efficiency should lead to a reduced fee or different value proposition. Ethical rules around fees (Texas Disciplinary Rule 1.04) require that fees be reasonable.

Conclusion: Embracing Innovation Responsibly

AI offers significant potential to enhance legal practice and deliver more cost-effective services, but its responsible adoption is non-negotiable. The lawyer remains ultimately responsible for the accuracy and quality of all work product. The risks of AI "hallucinations" and confidentiality breaches underscore the enduring value of human legal expertise, diligence, and ethical practice.

For attorneys exploring the integration of AI, the path forward is one of cautious yet confident exploration. Education, robust safeguards, and a commitment to professional oversight are key. By leveraging AI wisely, legal professionals can enhance their practice, providing the best possible representation for clients while ensuring both efficiency and integrity.

Want To Learn More? Click below to read other posts by Larman Law in Heath, Texas: