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Prompt Engineering

· 9 min read
AI Technical Legal AI Terms

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1. Overview

Prompt engineering, at its core, is the art and science of crafting effective instructions for artificial intelligence (AI) systems, particularly large language models (LLMs). Think of it like giving instructions to a highly intelligent, but somewhat literal, intern. A poorly worded instruction can lead to irrelevant, inaccurate, or even harmful results. A well-crafted prompt, on the other hand, can unlock the AI’s potential to perform complex tasks, from legal research and document drafting to contract analysis and risk assessment. For legal professionals, understanding prompt engineering is becoming increasingly crucial because it directly impacts the quality, reliability, and ethical implications of using AI in legal practice. The ability to effectively guide these AI systems is no longer just a technical skill; it’s becoming a necessary competency for responsible and effective legal practice in the age of AI.

Imagine you’re asking a research librarian to find cases related to a specific legal issue. A vague request like, “Find me cases about contracts,” will likely yield thousands of irrelevant results. However, a specific request like, “Find me cases from the Second Circuit Court of Appeals between 2018 and 2023 concerning the enforceability of non-compete clauses in employment contracts for software engineers, focusing on situations where the employee was terminated without cause,” will result in a much more targeted and useful selection of cases. Prompt engineering is essentially the process of formulating that specific, targeted request for an AI system.

2. The Big Picture

Prompt engineering focuses on designing and refining the input provided to an AI model to achieve a desired output. The goal is to elicit the most accurate, relevant, and helpful response from the AI. It’s important to understand that LLMs don’t “think” like humans; they operate based on patterns learned from massive amounts of data. Therefore, the way you phrase your request significantly influences the AI’s interpretation and subsequent response.

Key concepts in prompt engineering include:

  • Clarity and Specificity: The more specific and unambiguous your prompt, the better the AI can understand your intention. Avoid vague language and provide as much relevant context as possible.

  • Contextual Information: Providing background information helps the AI understand the task at hand. For example, if you’re asking the AI to analyze a contract, include details about the type of contract, the parties involved, and the relevant jurisdiction.

  • Instruction Type (Task Definition): Clearly define the task you want the AI to perform. Are you asking it to summarize a document, answer a question, generate text, or translate a language? Explicitly stating the desired action guides the AI’s response.

  • Constraints and Limitations: Specifying constraints can improve the quality and relevance of the AI’s output. For example, you might limit the length of the response, specify the format (e.g., bullet points, table), or restrict the sources the AI can use.

  • Few-Shot Learning: Providing examples of the desired input-output relationship can significantly improve the AI’s performance. This is like showing the AI a few examples of how you want the task done before asking it to perform the task on its own.

Think of it like preparing a detailed brief for an associate attorney. The more information, context, and specific instructions you provide, the better the associate can understand the task and deliver the desired result. Similarly, prompt engineering provides the AI with the necessary guidance to generate a useful and reliable output.

3. Legal Implications

The rise of prompt engineering raises several critical legal implications that legal professionals must consider:

  • IP and Copyright Concerns: The output generated by an AI model is influenced by the data it was trained on, which may include copyrighted material. If a prompt instructs the AI to generate content that infringes on existing copyrights, who is liable? Is it the user who crafted the prompt, the developer of the AI model, or both? This raises complex questions about authorship, ownership, and liability in the age of AI. Furthermore, the copyrightability of AI-generated content remains a contentious issue, with legal opinions differing on whether such content can be protected by copyright [U.S. Copyright Office - https://www.copyright.gov/ai/]. For example, if a lawyer uses an AI to draft a legal brief based on a specific prompt, can the lawyer claim copyright over the resulting brief? The answer is currently unclear and subject to ongoing legal debate.

  • Data Privacy and Usage Issues: AI models are trained on vast amounts of data, which may include personal information. When a user provides a prompt, they are essentially feeding data into the AI system. This raises concerns about data privacy, especially if the prompt contains sensitive or confidential information. Legal professionals must be aware of the potential risks of disclosing client data through prompts and ensure that they comply with relevant data privacy regulations, such as GDPR and CCPA. Furthermore, it’s crucial to understand how the AI model uses and stores the data provided in prompts. Many AI service providers have privacy policies that outline their data handling practices, but legal professionals should carefully review these policies and ensure they are consistent with their ethical and legal obligations [OpenAI - https://openai.com/policies/].

  • Impact on Litigation: Prompt engineering can significantly impact litigation in several ways. First, AI-generated evidence, such as summaries of documents or analyses of data, may be introduced in court. The admissibility of such evidence will depend on its reliability and accuracy, which are directly influenced by the quality of the prompt. Legal professionals must be able to demonstrate that the prompts used to generate the evidence were carefully crafted and validated to ensure their accuracy and objectivity. Second, prompt engineering can be used to challenge the validity of AI-generated evidence. Opposing counsel may argue that the prompts were biased or misleading, leading to unreliable or inaccurate results. Therefore, it’s essential to maintain a clear record of the prompts used and the rationale behind them. Finally, the use of AI in legal research and strategy can create an asymmetry of information between parties, potentially disadvantaging those who lack access to or expertise in AI technologies. This raises concerns about fairness and equal access to justice.

  • Professional Responsibility and Ethical Obligations: Legal professionals have a duty to provide competent and diligent representation to their clients. The use of AI tools, guided by prompt engineering, must be consistent with these obligations. Lawyers must understand the limitations of AI and avoid over-reliance on AI-generated outputs. They must also ensure that the use of AI does not compromise client confidentiality or create conflicts of interest. Furthermore, lawyers have a responsibility to stay informed about the evolving legal and ethical landscape of AI and to use AI responsibly and ethically [ABA Model Rules of Professional Conduct - https://www.americanbar.org/groups/professional_responsibility/policy/model_rules_of_professional_conduct/].

4. Real-World Context

Prompt engineering is being used by a wide range of companies across various industries, including the legal sector.

  • Lex Machina (LexisNexis): Uses AI to analyze litigation data and predict outcomes. Prompt engineering can be used to refine the queries used to extract relevant information from court filings and case documents [Lex Machina - https://lexmachina.com/].

  • ROSS Intelligence: (Acquired by Thomson Reuters) Was an AI-powered legal research platform. Prompt engineering was crucial for formulating effective search queries and extracting relevant legal precedents [Thomson Reuters - https://www.thomsonreuters.com/].

  • Kira Systems (Now part of Litera): Uses AI for contract analysis and due diligence. Prompt engineering can be used to customize the AI’s analysis to identify specific clauses or provisions in contracts [Litera - https://www.litera.com/].

Real Examples from Industry:

  • A law firm uses prompt engineering to train an AI model to summarize depositions. By providing specific prompts that focus on key issues and witness testimony, the firm can quickly extract relevant information from lengthy transcripts.

  • A corporate legal department uses prompt engineering to analyze contracts for potential risks. By crafting prompts that target specific clauses, such as indemnification or limitation of liability, the department can identify contracts that require further review.

  • A legal tech company uses prompt engineering to develop a chatbot that can answer basic legal questions. By training the chatbot with a variety of prompts and responses, the company can provide users with instant access to legal information.

Current Legal Cases or Issues:

  • The use of AI in legal research and document review is raising questions about the unauthorized practice of law. Some argue that AI tools are simply augmenting the work of lawyers, while others contend that they are performing tasks that traditionally require a law license. This issue is being debated in various jurisdictions and may lead to new regulations on the use of AI in legal practice.

  • The reliance on AI-generated evidence in court is raising concerns about transparency and accountability. Critics argue that the “black box” nature of AI models makes it difficult to understand how they arrive at their conclusions, making it challenging to challenge their accuracy. This issue is prompting calls for greater transparency in the development and deployment of AI systems in the legal system.

5. Sources

By understanding the principles of prompt engineering and its legal implications, legal professionals can harness the power of AI while mitigating the risks and ensuring ethical and responsible use of this transformative technology.

Generated for legal professionals. 1715 words. Published 2025-10-26.

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