Please enjoy this ILTA Just-In-Time blog post by Iman Badri, Data Scientist, Munger, Tolles & Olson LLP.
Understanding Prompt Engineering
Prompt engineering can be described as the process of designing and fine-tuning input prompts to maximize the efficiency and accuracy of interactive AI systems responses. It involves crafting specific instructions, questions, or scenarios that help the AI system to understand the context and detailed requirements of the task. To achieve optimal outcomes, it is essential to have a good grasp of how AI / GenAI systems work and what they can and cannot do, and also to have a solid understanding of the characteristics of the task in question. Effective prompt engineering can significantly improve the performance of AI systems outputs in various applications, from drafting documents, emails and contracts to conducting legal research, brainstorming and performing data analysis tasks.
Clarity and Precision
It is safe to say that the foundation of prompt engineering is clear and accurate communication. A well-crafted prompt should leave no room for ambiguity, ensuring that the AI system understands the request correctly and accurately. As an example, legal professionals may take advantage of some AI / GenAI tools to draft complex corporate contracts, such as partnership agreements, merger agreements, or supply contracts. A simple prompt like "Draft a partnership agreement" is too vague and general and might lead to varied results. A more precise and clearer prompt would be: "Draft a partnership agreement for two companies entering a joint venture, including clauses for profit sharing, responsibilities of each party, duration of the partnership, dispute resolution mechanisms, and exit strategies". Providing these details helps the AI system to generate a more accurate and comprehensive agreement tailored to the specific requirements of a corporate partnership.
Contextual Relevance
It is essential to provide enough context while crafting an efficient prompt. The AI system should be able to understand the context in which it operates, to generate relevant and coherent responses. For example, when conducting legal research, the AI system needs to understand the context of the request. In this situation, instead of asking, "What are the implications of the new environmental regulation?" a more contextually relevant prompt would be: "Analyze the implications of the new environmental regulation on the manufacturing sector in the United States, focusing on emission standards, compliance costs, and potential penalties for non-compliance." This prompt gives the AI system a clear context and provides enough details, which will result in more targeted and accurate results.
Specificity in Output Requirements
Specifying the desired output format and structure that the user expects can significantly improve the quality of the final outcome of the AI-generated content. This principle makes sure that the output matches the user's specific requirements and expectations. For example, one of the common tasks that legal professionals usually perform is to summarize statutory changes. In this case, a generic prompt like "Summarize the recent changes to the tax code" might not result in the best expected result. Instead, specifying the desired structure and format can be more effective: "Provide a one-page summary of the recent changes to the U.S. tax code, including the main revisions, their impact on corporate tax rates, and any new compliance requirements for businesses." This prompt will guide the AI system to generate a precise and relevant summary that directly addresses the expected requirements of the expert dealing with tax law.
Iterative Refinement
For many complex situations, such as those that can occur in legal settings, prompt engineering is not a single-step activity, but an iterative process. This means that better outputs can come from improving prompts based on initial outputs through a few iterations. Suppose an AI chatbot provides automated legal advice to clients. The initial prompt might be: "Provide legal advice for starting a small business." based on previous principals, it would not be a surprise if the output of such a prompt is too broad. The iterative refinement principle suggest that this prompt can be refined iteratively, for example: "Provide legal advice for starting a small business in the state of New York, focusing on business structure options, necessary permits, and tax implications." Each iteration hones the AI system's response, making it more aligned with the user's needs and if the response is not still aligned with what the user expects, the next iteration might be necessary.
Leveraging Domain-Specific Knowledge
Using domain-specific knowledge in prompts improves the pertinence and precision of AI system outputs. This principle is particularly crucial in specialized fields like legal. For instance, drafting patent applications requires specific legal and sometimes technical knowledge. A generic prompt like "Draft a patent application for a new software invention" might not be enough for an accurate and relevant response. A more informed prompt would have a comprehensive structure to include domain-specific knowledge: "Draft a patent application for a new software invention that improves data encryption methods, including claims, ‘a detailed description of the invention in technical terms’, ‘prior art references’, and ‘potential applications / use cases’." This prompt structure leverages domain-specific knowledge and a well-structured content to properly guide the AI system in producing a more comprehensive and technically and legally sound document.
Ethical Considerations
Prompt engineering must always be done with ethics in mind, especially in a delicate area like the legal where content produced by AI systems can have serious implications. This discussion does not cover all the ethical considerations related to AI systems. However, one important challenge related to prompt engineering is that AI systems and large language models can sometimes (unintentionally) replicate biases that are present in their training datasets. When designing prompts, it's essential to consider and mitigate these types of biases to the extent that is technically possible. For example, instead of asking, "What are common reasons for divorce?" a more ethically sound prompt would be something like: "What are the legally recognized grounds for divorce in New York, and how do they impact divorce proceedings? This approach will likely reduce the chance of reinforcing stereotypes and relying on factual and legal criteria.
User-Centric Design
This principle (like some others) may seem obvious, but it is vital to design prompts that generate outputs that are particularly relevant and helpful to the user. This means understanding what exactly the end-user expects and needs from the AI system. For instance, legal professionals may need to use an AI / GenAI system to draft client correspondence. A user-centric prompt might be something like: "Draft a letter to a client explaining the next steps in their personal injury case, including an overview of the case status, upcoming court dates, and what to expect during the trial and also potential challenges or concerns." This prompt ensures that the AI-generated document addresses the client's immediate concerns and provides clear, structured and actionable information.
Enhancing Legal Practice with AI / GenAI
The legal industry has already started to experience the impact of AI and GenAI on different aspects of legal practice. By leveraging the power of prompt engineering and applying the universal principles of prompt engineering, legal practitioners can leverage the full power of AI systems and models to optimize their processes, support decision-making, and deliver better service to their clients.
Future Prospects and Other Considerations
As AI and GenAI continue to evolve, the role of prompt engineering in legal practice will become increasingly critical. The foundations discussed in this post cover some basic principles that help legal professionals to interact with AI and GenAI systems more efficiently and use their potential to improve their work in various ways. These foundations are some of the main and simple to grasp and use principles of prompt engineering, but they do not cover specific methods and more advanced techniques such as chain of thought (COT), zero shot prompting, active prompt, emotion prompting, scratchpad prompting, take a step back prompting, and different other techniques that can also be helpful in certain situations. By mastering the art of prompt engineering, legal professionals can unlock new levels of efficiency, accuracy, and innovation in their day-to-day activities. Prompt engineering is emerging as a cross-disciplinary skill with significant impacts on various sectors, especially in the intricate and delicate area of law where there is a lot of information that needs to be handled and precision is crucial in almost every activity. By leveraging the principles of clarity, contextual relevance, specificity, iterative refinement, domain-specific knowledge, ethical considerations, and user-centric design, legal professionals can harness the power of AI and GenAI systems to transform their workflows in variety of different ways. AI role is increasingly growing in the evolution of legal practice towards a brighter future, and prompt engineering is one of the main components of this revolutionary process.
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