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Mastering AI Prompts for Legal Professionals in 2024

Explore our guide on AI prompts tailored for legal professionals. Learn to integrate AI tools, enhance efficiency, and navigate ethical considerations.
woman at computer using generative ai for legal work

Artificial Intelligence (AI) is not just a buzzword; it’s rapidly becoming an essential tool in various industries, particularly legal ones. As we witness the rise of Generative AI (GenAI), upskilling has become even more critical for legal professionals. This blog aims to shed light on AI prompts, what to keep in mind, and how to use them in today’s fast-evolving legal landscape.

AI technologies are revolutionizing the way legal services are rendered by automating repetitive tasks, processing vast amounts of data instantly, and streamlining contract drafting and review. But AI goes beyond just executing programmed tasks. It learns and improves through human feedback, enabling legal professionals to make better, data-driven decisions.

While AI will never replace human intelligence or the nuanced expertise of human lawyers, it will continue to significantly impact practices within law firms and in-house counsel professions. Currently, AI helps with: 

  • Draft legal documents
  • Perform due diligence
  • Conduct legal research
  • Simplify sophisticated cases
  • Provide prediction analytics
  • Review documents for errors

Below, we explore the significance of AI, especially AI prompts, for lawyers and why they are important. 

Different Types of AI Technologies

There are six primary types of AI technology being used in today’s legal procedures:

image depicting the 6 primary types of ai technology used in legal

Machine Learning (ML)

This does not require direct programming but rather improves performance through experience. Humans provide data input, and the machine seeks patterns to suggest conclusions. It continues to do this while applying previous conclusions to future data. It grows “smarter” over time. 

LEGAL USE CASE EXAMPLE: Recommend citations to case law based on semantic similarity.

Deep Learning

The system utilizes deep neural networks with multiple layers and learns from large amounts of data to enhance voice control and image recognition. 

LEGAL USE CASE EXAMPLE: Predicting case outcomes and judicial decisions by identifying patterns and correlations. 

Natural Language Processing (NLP)

Enables machines to understand, interpret, and generate human language. It is used in chatbots, language translation services, and sentiment analysis applications. 

LEGAL USE CASE EXAMPLE: Draft a purchase agreement, service agreement, lease agreement, or another legal document that mimics human writing, as well as plain language queries and interaction with tools. 

Generative AI

Generative AI refers to a class of artificial intelligence algorithms that can generate new content, such as text, images, music, or even code, based on the patterns and data they have been trained on. These algorithms, often based on deep learning models can create outputs that are coherent and contextually relevant. 

LEGAL USE CASE EXAMPLE: GenAI Drafts a non-disclosure agreement (NDA) for a client. By using a generative AI tool, a lawyer can input specific requirements, such as the parties involved, the duration of the agreement, and any specific clauses needed. The AI then generates a comprehensive NDA draft, which the lawyer can quickly review and finalize, ensuring all client needs are met efficiently. 

Computer Vision

Teaches machines to interpret the world visually, such as videos and images. It is often used in autonomous vehicles, object detection, medical image analysis, surveillance, and facial recognition. 

LEGAL USE CASE EXAMPLE: Visual, auditory, and other discovery and litigation assistance.

Expert Systems

Answer questions and solve problems like a human using rule-based systems and inference engine. 

LEGAL USE CASE EXAMPLE: Provide contract drafting and case management advice and recommendations.

chart showing predictions for the future of the legal profession

AI is not only here, it is here to stay and will continue gaining momentum. A recent study by Gartner found that, on average, business leaders plan on allocating 6.5% of their budget to generative AI. While AI can’t replace lawyers’ analytical skills or their ability to deliver important client relations, AI can, in fact, perform some legal responsibilities that were once handled by humans. And its capabilities are becoming more prevalent in legal practices.

Below are a few examples of where AI is headed:

More Predictive modeling tools

AI-powered software leverages algorithms to analyze a tremendous amount of data and forecast accurate predictions in the legal industry. 

  • Resource allocation based on forecasted workload
  • Predict the success of a case based on historical data
  • Calculate non-compliance risks
  • Cite legal precedents that impact case outcome

Using this tool and its data, attorneys can make better decisions, build confidence, or make necessary changes to improve their chances of success. 

AI in Law Schools

The way law students and professionals are approaching AI varies across schools. Some professors are introducing AI into the curriculum by allowing students to use it for research and even in assignments. Schools are integrating AI-specific courses that focus on the correct way to use the technology. 

Nobody likes to receive a bill that has gone over budget or is not what was agreed upon. Using AI, lawyers can analyze historical data of related cases to better understand potential attorney hours required, court fees, expert expenses, administrative costs, and other associated fees. This helps maintain positive client relationships, builds trust, and allows the finance department to predict revenue. It also allows legal professionals to assess whether pursuing the business transaction aligns with the end goals. 

Create More Jobs

While some worry that AI will replace humans, the truth is that AI has great potential to actually create more jobs in the legal space. From engineers who develop generative AI or write prompts to legal professionals tasked with reviewing AI’s drafts or analytics, more and more people will be needed to continue AI’s rapid growth. 

Crafting Effective AI Prompts

Gartner’s Generative AI 2024 Planning Survey revealed that 18% of business leaders are piloting, implementing, or have implemented generative AI (GAI) for their functions, while 47% will do so in the coming 12 months.

When using different GAI models, in order to receive the desired outcome, a user must input a prompt. A prompt is a statement or question that uses keywords to ask for an output. Prompts can range from a single word to a whole paragraph, but it is crucial that it is specific and clear to produce the desired response. The better the prompt, the better the answer. Just like a person, AI can only generate useful responses if it completely understands the request. 

An effective prompt delivers clear and concise instructions without room for misinterpretation. It should also provide adequate context to guide better understanding, such as case details, law firm information, practice area, etc.  

It’s important to learn the system’s balance between too much information or too little. And nothing is set in stone. If the first prompt doesn’t return an answer that you like, try again. “Playing” with prompts and experimenting with different approaches allows you to gain better, more targeted responses. When you initially start using AI prompts, it’s ok to start with simple, manageable requests. Learn what works and what doesn’t. Taking a practical approach allows you to become more familiar with the technology and also understand its limitations. Keep in mind that using AI is a collaborative effort between the human and the technology. 

The ABCs...and DEs of building a legal AI prompt

AI Prompts for Lawyers: Examples

Good: What is the termination clause in the contract?

DEs (Key Elements Applied):

  • Accurate: The question is accurate but very general.
  • Blunt: It is blunt and to the point.
  • Clarity: It has clarity but lacks detail.

Better: Locate the termination provisions. Summarize the key conditions, notice periods, and any other relevant information related to terminating the agreement.

DEs (Key Elements Applied):

  • Accurate: The prompt is more precise in specifying what needs to be located and summarized. 
  • Blunt: It is still straightforward. 
  • Clarity: There is greater clarity in the specific aspects to be summarized (key conditions, notice periods, other relevant information). 
  • Detailed: It provides more detail about what exactly to look for and summarize. 

Good: Who are the parties involved in the non-disclosure agreement?

DEs (Key Elements Applied):

  • Accurate: The question is clear and accurate. 
  • Blunt: It is blunt. 
  • Clarity: It has clarity but lacks depth. 

Better: Extract the names and roles of all parties involved in the non-disclosure agreement. Provide a summary of each party’s key responsibilities and obligations.

DEs (Key Elements Applied):

  • Accurate: The prompt is accurate and specific about what to extract. 
  • Blunt: It is straightforward. 
  • Clarity: There is enhanced clarity in specifying the roles and responsibilities to be summarized. 
  • Detailed: It provides detailed instructions on what information to extract and summarize. 

Good: What are the confidentiality obligations mentioned in the confidentiality agreement?

DEs (Key Elements Applied):

  • Accurate: The question is accurate but lacks specificity. 
  • Blunt: It is blunt. 
  • Clarity: It has clarity but needs more details. 

Better: Extract any confidentiality obligations or non-disclosure agreements. Identify the scope of the confidentiality, the parties bound by it, and the duration of the obligations. Provide the relevant excerpts from the documents.

DEs (Key Elements Applied):

  • Accurate: The prompt accurately specifies what to extract and identify. 
  • Blunt: It remains straightforward. 
  • Clarity: The clarity is improved by specifying what aspects of the obligations to identify (scope, parties, duration). 
  • Detailed: It includes detailed instructions on what to extract and identify. 
  • Examples: By specifying what to identify (scope, parties, duration), it indirectly provides examples. 

 In summary, the “better” prompts utilize all the elements (A, B, C, D, and E) more effectively than the “good” prompts, making them more precise, clear, and detailed. 

If you’re looking to learn more and gain more expertise on prompting for legal items, Professor Dennis Kennedy at Michigan State University has offered an AI Prompting Workshop and provides guides to improve your prompting skills beyond what we cover in this article.

Professor Kennedy suggests the PRCO method, which is also very helpful:

  • Persona: Define the persona to fit the intended context or audience,
  • Context: Provide well-defined context that helps the Persona you have work on the problem
  • Request: Frame the task or question with precision, not general ideas
  • Outcome: Tell the AI the scope, purpose, and output you are seeking to achieve precise needs

A piece of good news is that much of this is already accomplished when you select ContractPodAi’s Leah Legal AI!

Practical Applications and Use Cases 

AI for Contract Review

Technique: Leveraging AI-powered algorithms to analyze contract language for potential risks, inconsistencies, and compliance issues, enhancing the redlining process.

Prompt Example: “Identify clauses with high-risk terms in this employment contract.”

Use Case for Demonstrating Effective Use:

  • Scenario: A contract manager is tasked with reviewing a large number of employment contracts for a multinational corporation, aiming to identify and mitigate risks quickly.
  • Solution: By utilizing an AI-based contract review tool, the contract manager inputs the query: “Highlight clauses with high-risk terms in these contracts.” The AI scans through the documents, flags high-risk clauses such as non-standard termination conditions or ambiguous confidentiality terms, and provides suggestions for revisions, allowing the manager to streamline the review process and ensure compliance effectively.

Technique: Employing AI-driven tools to assist in drafting legal documents by auto-generating text based on predefined templates, legal standards, and specific client requirements.

Prompt Example: “Generate a standard non-disclosure agreement for a freelance contractor.”

Use Case for Demonstrating Effective Use:

  • Scenario: A paralegal is responsible for drafting multiple standard contracts for freelancers in a tech startup, ensuring all necessary legal protections are included.
  • Solution: Using an AI-powered drafting tool, the paralegal inputs the query: “Create a non-disclosure agreement for a freelance contractor.” The AI generates a comprehensive NDA based on current legal standards and tailored to the specific needs of the company, including clauses on confidentiality, intellectual property rights, and dispute resolution. This enables the paralegal to efficiently produce accurate, legally sound documents with minimal manual effort.

Technique: Applying AI techniques to extract key information and data points from legal documents, such as dates, parties involved, obligations, and critical terms.

Prompt Example: “Extract all relevant dates and party names from this lease agreement.”

Use Case for Demonstrating Effective Use:

  • Scenario: A legal assistant needs to pull out critical data from a stack of lease agreements to populate a central database quickly.
  • Solution: With an AI-powered legal extraction tool, the assistant inputs the query: “Extract dates, party names, and obligation details from these lease agreements.” The AI processes the documents, identifying and extracting the necessary information, such as lease start and end dates, names of lessors and lessees, and key obligations like payment terms and renewal conditions. This streamlined extraction process enables the assistant to compile a comprehensive, accurate data set efficiently, saving significant time and reducing the risk of human error.

Technique: Utilizing machine learning algorithms to analyze and categorize vast amounts of legal documents, emails, and other data sources relevant to legal discovery in lawsuits.

Prompt Example: “Identify and classify relevant documents for the discovery phase in a securities fraud case.”

Use Case for Demonstrating Effective Use:

  • Scenario: An attorney is involved in a securities fraud lawsuit and needs to sift through a massive amount of corporate documents to find relevant evidence for discovery.
  • Solution: By employing an AI-driven legal discovery tool, the attorney inputs the query: “Classify all relevant documents for this securities fraud case.” The AI scans and categorizes thousands of documents, emails, and other data sources, identifying key pieces of evidence based on contextual relevance, keywords, and patterns. This tool significantly accelerates the discovery process, ensuring a thorough and efficient review of documents, which allows the attorney to focus on developing a strong case strategy.  

AI for Case Law Research 

Technique: Utilizing natural language processing (NLP) to parse and understand complex legal language, making it easier to search for relevant case law based on specific queries. 

Prompt Example: “Find landmark Supreme Court cases related to intellectual property disputes.” 

Use Case for Demonstrating Effective Use:

  • Scenario: A lawyer needs to prepare for an intellectual property case and requires precedents that can strengthen their argument. 
  • Solution: Using an AI-powered legal research tool, the lawyer inputs the query: “Cases where the Supreme Court ruled on software patent disputes.” The AI retrieves relevant cases, summaries, and key legal principles, allowing the lawyer to quickly identify critical precedents and build a robust legal strategy.  

AI in Compliance and Regulatory Matters 

Technique: Implementing AI to monitor regulatory bodies and legal publications for updates and changes in laws and regulations. 

Prompt Example: “Alert me to any changes in data privacy regulations in the European Union.” 

Use Case for Demonstrating Effective Use:

  • Scenario: A compliance officer at a multinational corporation needs to ensure that the company remains compliant with ever-evolving data privacy regulations in various jurisdictions.
  • Solution: Using an AI-driven compliance monitoring tool, the compliance officer sets up a query like: “Notify me of updates to data privacy regulations in the European Union.” The AI continuously scans official regulatory databases and legal publications. When there is a new development or update, the tool sends an alert summarizing the changes, allowing the compliance officer to adjust company policies and practices to maintain compliance promptly.

Fortunately, ContractPodAi lets you take the guesswork out of effective prompts by building them into the technology. ContractPodAi’s Leah Legal AI, designed exclusively for legal and compliance use cases, leverages best-of-breed Large Language Models (LLMs).

Leah, your own personalized GenAI solution, makes it effortlessly simple to perform legal responsibilities faster, smarter, and with total confidence. It incorporates ethical guardrails and rigorous testing, and her actions align with your organization’s standards, fostering trust in AI solutions. Leah empowers strategic thinking and offers real-time, precedence-based legal analysis. 

Leah AI offers a variety of specialized legal modules that offer cutting-edge GenAI and are rigorously tested for maximum accuracy. Each module is powered by tailored frameworks for specific legal tasks to ensure efficiency and reliable results for your unique workflow. Modules include Extract, Redline, Discovery, Deals, Claims, Playbook, Helpdesk and Draft.

Leah AI is tailored specifically for contract management and legal operations, including contract negotiations. Within minutes, she can produce results that significantly enhance your legal workflows: 

  • Analyze your contracts and create a record 
  • Identify key clauses, compare them to your historical data, and highlight relevant insights for faster, more informed negotiations. 
  • Find favorable language from your past legal documents, suggest data-backed counterpoints, and alert you to potential risks based on your established legal framework. 
  • Provide proactive guidance based on successful past negotiations 
  • Suggest clauses, terms, and redlines aligned with your company’s objectives and proven strategies 
  • Offer insight into all your vendor and customer contract data 
  • Accelerate your negotiations with real-time data powered by predictive analytics, served up in a visual dashboard. 

Leah AI is transforming the way legal professionals do their jobs. She can be used for drafting legal documents, conducting exhausting legal research, automating document management, analyzing contracts, and streamlining communication. And all of this can be done faster than humans, which helps save time for the business and client. It also enables lawyers to increase their productivity by focusing on more strategic responsibilities and more frequent client communication. 

Increased Productivity

Leveraging AI in the legal field has proven to drastically increase legal professionals’ productivity rate. In fact, a Gartner survey reported that 30% of law firms noticed an increase in productivity since incorporating AI into their practices. Leah AI handles mundane, repetitive tasks so you can handle more legal matters with more energy, attention, and time. 

Leveraging AI in the legal field has proven to drastically increase legal professionals’ productivity rate. In fact, 48% of young lawyers believe AI will significantly change how they will work within the next five years. Leah AI handles mundane, repetitive tasks so you can handle more legal matters with more energy, attention, and time. 

Improved Accuracy

Unlike humans, Leah never tires and never has conflicts on her calendar. She is always ready to work and provides accurate, timely information fast while minimizing the margin of error.   

Client Focused

Building positive relationships both with internal teams and external clients is imperative for a successful legal practice. Allowing Leah AI to handle more time-consuming tasks will free up time to engage with clients and for answering client questions in a timely manner. 

Financial Savings

According to Zion Market Research, it is anticipated that legal AI software market will reach $5,000 million by 2030. Fortunately, businesses investing in AI see financial benefit as legal professionals are able to dedicate more time to billable hours, strategic planning and improved customer engagement. Allocating legal administrative duties to AI reallocates human resources to grow the organization’s profitability. 

Some LLMs are better at specific tasks than others. For example, some excel at generating content, while others are ideal for concluding advanced analysis. As customers onboard GenAI solutions, they realize that a one-size-fits-all LLM strategy falls short at the enterprise level. Relying on one model can lead to limitations, especially for specialized tasks and advanced use cases. 

Users need to ensure models align with their organization’s legal and compliance knowledge base, including legal analysis, reviewing and redlining, or drafting legal documents.  

While AI is certainly finding its place in the future of legal practices and creating many promising opportunities, there are ethical considerations associated with it as well. In fact, in August 2019, the American Bar Association adopted a resolution urging courts and lawyers “to address the emerging ethical and legal issues related to the usage of artificial intelligence (“AI”) in the practice of law.”

Legal professionals who leverage AI and developers who create it are responsible for using it responsibly and ethically. Users must be educated on how the technology works and fully understand its capabilities and limitations.

Considerations include:

Bias and fairness

Two major technical factors that contribute to AI bias are training data and programming errors.

Because AI is used for its data analysis, it can inadvertently pull biased historical information. If this information is used in law practice, it has the potential to produce unfair results and cause discrimination. The two major technical factors that contribute to AI bias are training data and programming errors. AI technology develops their decision-making based on training data. However, when that data overrepresents or underrepresents certain groups, it can cause biased results. Also, mislabeled data or data that reflects existing inequalities can create additional issues. For example, if a law firm is using AI to hire new lawyers, but the applicant qualifications are not labeled correctly, the system can reject qualified applicants and suggest less qualified candidates. 

Programming errors occur when coding mistakes are made by the developer. For instance, a developer might have a bias and either consciously or unconsciously overweigh certain factors. A real-world example of this is a credit card company using an AI algorithm that demonstrates social bias by advertising their products by targeting less-educated people with offers featuring higher interest rates. This audience then clicks on the ads without knowing that other social groups are shown better offers. 


We’ve shared that a primary benefit of AI for lawyers is its accuracy. However, inaccurate results can wreak havoc for law firms and businesses alike. When AI-generated content is not accurate, it is referred to as “hallucinations”. Lawyers must meticulously review any content that is suggested or edited by AI. For example, in June 2023, a lawyer involved with a personal injury case relied on ChatGPT to prepare a filing. However, his brief included false case citations, aka hallucinations, and he was reprimanded, which had a negative impact on the judicial process.

Accuracy is also very important in translation. Lawyers must be completely sure that the language model is interpreting testimony precisely in order to ensure the integrity of the testimony.


Protecting sensitive data is an inherent goal in any industry. It become extremely critical in the legal sector, as data is nearly always confidential. AI tools must have proper security to avoid breaches and privacy violations, especially if information is shared with third-party service providers or on Cloud platforms. Law firms and companies must deploy the right technology and craft proven policies, guidelines, and practices to prevent security issues. Policies should include:  

  • Approved types of data that can/can’t be fed into generative AI tools
  • Requests and potential AI use cases that should escalate to legal for approval
  • Proper precautions associated when using AI tools


Even though artificial intelligence offers tremendous support for lawyers and workers in the legal field, lawyers are still entirely responsible for their own work. This means they must be proactive and fully engaged when implementing AI in their legal practices. AI technology should complement their legal work, not replace it. 


The practice of law continues to change in the face of the digital age. Integrating AI is becoming the norm, which presents both great opportunities and some challenges for legal practitioners. Knowing how to choose the right technology, utilize refined prompts when necessary, and gain continuous knowledge will only add positive implications to a law firm or in-house lawyers. While ethical considerations must always be considered, AI is an undeniably useful technology that is here to stay. 

If you want to know more about our Legal GenAI solution, Leah, contact us today and request a demo.

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