Author: Onit

Feeling comfortable with core prompting concepts? Great — now it’s time to take the next step with integrating AI into your Legal workflows. Let’s walk through some examples to implement these skills. You can use any AI tool (ChatGPT, Anthropic) to illustrate these different prompting techniques.

Feel free to follow along by creating your own prompts, inputting them into the tool, or simply reviewing the examples provided. You can copy and paste the sample prompts into ChatGPT to test it yourself.

After each prompt, think about ChatGPT’s response and how you might refine the prompt using techniques like interactive dialogue or iterative refinement. The prompts below aim to demonstrate ways legal professionals can collaborate with AI to get the insights they need.

Exercise 1: Basic Legal Prompting

Basic Objective:
Have AI summarize a legal contract.

Contract Sample to Summarize:
“THIS AGREEMENT entered into this 1st day of January 2023, by and between Party A, a corporation organized under the laws of the State of California (‘Party A’), and Party B, a corporation organized under the laws of the State of New York (‘Party B’). Both parties agree to maintain and protect the confidential information obtained during the course of this agreement, following the confidentiality clause outlined in Section 5.”

Persona and Specifics:
You are a Paralegal assisting a lawyer, and your role is to review and summarize key points of contracts. The lawyer needs quick understanding through clear and concise summaries of the essential contract content.

Objective:
Short Summary Points: Offer short, precise summaries that illuminate the crucial contract aspects like agreement parties, confidentiality obligations, and other significant rights or duties. Summaries should be brief yet encompassing, shedding light on the contract’s main elements without over-detailing.

Constraints:
Output Length: Limit each summary point to two sentences maximum, with the overall response not exceeding 1000 characters.

Examples (Few-Shot Prompts):
Input: “A clause in the contract defines the agreement parties.”
Output: “Agreement Parties: Party A (California-based) and Party B (New York-based) are engaged in this agreement, each with distinct rights and obligations.”

Input: “Section 5 of the contract outlines the confidentiality obligations.
Output: “Confidentiality: Both Party A and Party B are bound to protect and uphold confidential
information as detailed in Section 5 of the agreement.”

Accuracy:
Ensure summaries are exact and faithful to the contract’s text, avoiding assumptions and inaccuracies. Summaries should be strictly derived from the contract information.

Format:
Summaries should be presented in a bullet-point format. Each point must have a headline followed by a brief description, ensuring easy readability and understanding even for individuals not specialized in law.

AI Task:
Given the sample contract snippet above, craft a concise summary following the objective, constraints, examples, and format detailed in the Crafted Prompt for AI. Ensure your summary accurately reflects the contract’s content, facilitating quick and clear comprehension for the lawyer you are assisting.

Follow-up questions:
• Iterative Refinement: Ask it to summarize the key points in 3 bullet points instead
of full sentences.
• Interactive Dialogue: Could you clarify the confidentiality obligations – who is responsible for maintaining confidentiality?
• Chained Reasoning: What are the consequences if confidentiality is breached? And then, have it explained based on its previous summary.
• Socratic Questioning: What factors should be considered in determining if this confidentiality clause provides adequate protection?
• Self-Reflection: Review your summary. What are 1-2 ways you could improve the clarity or conciseness?

Exercise 2: Intermediate Prompting

Basic Objective:
Generate LinkedIn posts using AI based on an IDC MarketScape report.

Report Sample to Summarize:
The IDC MarketScape report content provided as input to AI.

Persona and Specifics:
You are an Enterprise Marketer working for a leading legal tech company. Your primary role involves creating engaging content for LinkedIn, blogs, and emails to inform and attract potential clients and partners.

Objective:
Short Summary Points: Deliver succinct, engaging LinkedIn posts capturing key findings and insights from the IDC MarketScape report. The focus should be on the unique capabilities and values of your company over competitors.

Constraints:
Output Length: Each LinkedIn post should not exceed 280 characters (standard LinkedIn post length), and the overall content generated should be close to 3000 tokens to yield multiple LinkedIn posts.

Examples (Few-Shot Prompts):
Input: “The IDC report mentions the unique capabilities of the leading legal tech
companies.”
Output: “Leading in legal tech! Our capabilities stand out in the latest IDC MarketScape report. Discover how we surpass competitors! #LegalTech #IDCReport2023”
Input: “The IDC report emphasizes the importance of business values.”
Output: “Business values at the forefront! The IDC MarketScape report echoes our
commitment to integrity and innovation. #LegalTechValues #IDCInsights”

Accuracy:
Ensure LinkedIn posts capture the essence of the IDC MarketScape report without misrepresentation. The posts should strictly adhere to the report’s findings while highlighting the company’s strengths..

Format:
Posts should be presented in a casual, engaging style suitable for LinkedIn. Each post must capture attention and motivate readers to learn more about the company and the report.

Temperature:
A temperature of 1 is set to encourage the AI to generate creative content. The temperature setting influences the randomness and creativity in the generated text, with higher values resulting in more creative outputs.

AI Task:
Given the sample IDC MarketScape report snippet above, craft LinkedIn posts following the objective, constraints, examples, and format detailed in this Crafted Prompt for AI. Ensure your posts accurately reflect the report’s content and promote the company’s unique position in the legal tech landscape.

Follow-up questions:
• Iterative Refinement: Can you reduce the length of this post while retaining its key message?
• Interactive Dialogue: What were the primary findings regarding our company in the IDC report?
• Chained Reasoning: Based on our company’s highlighted capabilities in the IDC report, how do we compare to our main competitor?
• Socratic Questioning: How does the report’s emphasis on business values differentiate us in the market?
• Self-Reflection: Review the posts you generated. Are there ways to make them more engaging or relevant to our target audience?

Want to learn more about how you can unlock the true potential of AI systems (including advanced prompting techniques)? Download our full eBook entitled The Legal Professional’s Handbook: Generative AI Fundamentals, Prompting, and Applications.

Integrating advanced legal AI tools like LLMs catalyzes a significant shift for in-house legal teams. These models are evolving from mere tools to invaluable partners, extending in-house professionals’ capabilities. Adopting legal AI software is a strategic decision for in-house teams that can transform service delivery, enhance productivity, and provide data-driven insights.

Here’s a closer look at five key factors to think about when integrating AI:

1. Cost Considerations

Immediate Efficiency Gains: AI automation of repetitive tasks like contract reviews can yield direct time savings, reducing manual hours spent.

Optimize Spend: The cost savings allow for investments in training, advanced AI tools, and other high-value initiatives rather than repetitive manual work.

2. Workflow Evolution

Reskilling: With AI excelling in routine tasks, in-house team members can take
on more complex responsibilities, upskilling into higher-value work.

Ongoing Learning: As AI evolves, so must in-house professionals’ skills. Regular AI training ensures everyone stays updated on the latest developments.

3. Data-Driven Insights

Instant Analysis: AI for legal documents can provide real-time insights from data that previously required extensive manual analysis. This empowers faster, informed decisions.

Proactive Risk Monitoring: AI analysis of contracts and documents can proactively detect risks, allowing preventative mitigation.

4. Change Management

Addressing Hesitancy: Hosting regular workshops provides a venue for hesitant team members to gain familiarity with AI systems in a collaborative setting. This can ease adoption.

User Feedback: Encourage continuous user feedback on AI tools. On-the-ground insights allow refinements tailored to team needs.

5. Integration with Other Technologies

Legal Tech AI Synergy with Blockchain: AI can help validate blockchain data beyond smart contracts, offering a more robust solution for secure transactions or records.

Collaborative Platforms: AI can seamlessly integrate with collaborative tools and platforms used by legal firms, ensuring a cohesive workflow. Whether it’s document collaboration or scheduling client meetings, AI can bring efficiency to these tasks.

Adaptive Systems: The beauty of modern AI is its adaptability. By connecting it with tools like CRMs or document management systems, it can learn and adapt based on historical data and user interactions.

Integrating AI is an ongoing journey requiring strategic planning, skills development, and a willingness to evolve. The payoff makes this effort invaluable for in-house productivity and insights. With thoughtful change management, AI transitions from an external tool to an intrinsic capability. Involvement and feedback from professionals is the key to ensuring the tech aligns with team needs. With meticulous implementation, AI becomes a seamless ally rather than a disruptive presence, propelling teams to new heights.

Want to learn more about how you can unlock the true potential of AI systems (including advanced prompting techniques)? Download our full eBook entitled The Legal Professional’s Handbook: Generative AI Fundamentals, Prompting, and Applications.

As AI capabilities progress, in-house legal teams have an invaluable opportunity to integrate these advanced technologies into key legal workflows and processes to drive greater efficiency, insights, and productivity. When thoughtfully implemented, legal AI can serve as an ally in handling high-volume, repetitive tasks that have traditionally burdened legal professionals’ time.

From contract management to legal research and beyond, AI systems powered by strong prompting skills can amplify and augment in-house teams’ efforts, allowing professionals to focus their expertise on the most strategic, high-value aspects of legal work.

Here are 7 key AI applications for in-house legal workflows:

1. Contract Analysis and Review: A well-crafted prompt can enable AI to sift through complex contracts meticulously, spotlight duties, identify potential risks, and offer actionable insights.
2. Invoice Auditing: AI can rapidly process high volumes of legal invoices, flagging potentially erroneous charges for auditors to review. This optimizes the invoice validation process.
3. Litigation Support and Preparation: AI assists with tasks like organizing case documents, drafting briefs, and finding supporting precedents to bolster arguments. This reduces repetitive preparation work.
4. Regulatory Monitoring: AI tracks updates across vast regulatory sources and alerts teams to key changes relevant to the business. This enables proactive compliance.
5. IP Management: Consider the herculean task of analyzing vast patent databases. With its efficiency, AI ensures exhaustive patent searches and assists in drafting applications with precision.
6. Discovery: AI expedites eDiscovery by quickly filtering huge document sets down to the most relevant materials, minimizing review time.
7. Legal Research: With thoughtful prompting, AI can rapidly traverse extensive legal databases, identifying pertinent cases, rulings, and regulations.

Integrating Legal AI into these critical in-house legal workflows with meticulous implementation and oversight can profoundly augment legal professionals’ capabilities and enable more strategic, high-value work. AI’s incorporation in legal practice is not just a pursuit of efficiency — it’s about refining the quality of legal services. As we harness AI’s prowess, a principle must be held sacred: AI tools, no matter how advanced, should serve as an extension of your expertise and not a replacement.

Want to learn more about how you can unlock the true potential of AI systems (including advanced prompting techniques)? Download our full eBook entitled The Legal Professional’s Handbook: Generative AI Fundamentals, Prompting, and Applications.

Well-crafted prompts are key to accurate, useful AI outputs. A prompt is your input to the LLM to guide its outputs. Essentially, it’s a question or statement the LLM is asked to respond to or build upon.

Prompts can range from a single word to a whole paragraph, depending on what the user is trying to achieve. LLMs use the information in the prompt as a basis for generating their response, so the quality and clarity of the prompt can significantly influence the answer.

Careful prompt design is key in instructing the LLM to produce the desired output. Vague prompts lead to confusion, but clear, detailed prompts elicit outstanding results. Framing prompts using the AI’s language gets the desired responses.

The First Step: Begin with Basics and Progress Gradually

When integrating AI into legal tasks, start with straightforward, manageable prompts. For instance, initially use AI to summarize legal documents or provide legal principles overviews. This practical approach allows you to familiarize yourself with AI’s functionalities and limitations while developing proficiency in crafting effective prompts.

It’s common to encounter challenges as you navigate this learning process. Rather than aiming for immediate perfection, view each challenge as an opportunity for constructive learning. These early experiences, even the difficult ones, lay the foundation for future success with AI.

Remember that success with AI is collaborative. Adjust your approach accordingly if a prompt doesn’t yield the expected results. Refine prompts, analyze responses, and iterate as needed. This hands-on practice is key to mastering prompting and interpretation.

As your skills develop, gradually introduce more complexity into prompts. Consistency in practicing core skills leads from proficiency in basics to efficiently handling advanced AI interactions. With a solid foundation, you’ll be well-equipped to fully harness AI’s potential for elevating legal work.

The 3Ps Prompting Framework

The 3Ps approach provides a structured way to guide AI systems through effective prompting. It consists of:

• PROMPT: This is the core instruction provided to the AI detailing exactly what you want it to do. A properly engineered prompt includes clarity, specificity, examples, constraints, and ample context to guide the system. The prompt is where you ask the AI for what you need, whether it’s a legal summary, analysis, document draft, or other output. An effective prompt maximizes accuracy. Combining thoughtful priming, persona setting, and a meticulously crafted prompt allows prompting at an expert level to get the most out of legal AI systems.
• PRIMING: Priming involves setting the stage and establishing the necessary context for the AI. Imagine you need to brief a junior lawyer on a case’s background before they can work on it; explaining the goals, facts, and history allows them to dive in effectively. Similarly, priming an AI lays the groundwork for success. Examples of priming include summarizing documents the AI needs to read for context, explaining the business objectives, client needs, or legal issues involved, or providing any required definitions or domain knowledge.
• PERSONA: You can specify a persona if you want the AI to adopt a specific perspective. This puts the AI in a certain mindset, similar to how lawyers think differently depending on their role, like prosecution vs. defense attorneys. Persona examples include patent lawyer (frames responses from a patent law point of view), plaintiff’s attorney (approaches issues from a plaintiff-favoring stance), and criminal prosecutor (considers implications in building a case against the accused).

Anatomy of a Strong Prompt

Now that we’ve covered the basics let’s dive into the anatomy of what makes an effective, robust prompt. What core attributes define a truly “strong” prompt?

Effective prompts contain:

• Clarity – Unambiguous, precise phrasing
• Specifics – Exact definitions of needed information
• Context Richness – Sufficient background information for depth and insight
• Good Structure – Clear formatting that aids comprehension
• Readability – Use simple, concise language.
• Examples – To illustrate desired outputs
• Constraints – Outline boundaries and limitations (output length or formatting, timeframe, geography, etc.).
• Accuracy – Avoiding errors that cause misleading results

Large language models are trained on extensive written text, making structural details like complete sentences and line breaks important for accurate responses. Constraints and examples guide the AI by setting expectations and a pathway to follow.

Every element of a prompt influences the AI’s response. Vague prompts confuse the AI, while focused, tight phrasing elicits spot-on responses. Constraints like length limits limit the scope. Examples guide better outputs. Each detail shapes the final result. Craft prompts carefully, considering how each component impacts the AI’s understanding.

Key Technical Settings

When using AI systems, there are specific settings you can adjust that impact how the AI responds. Knowing these key technical settings as a beginner will help you get better results.

• Creativity Setting: This controls how consistent or varied the AI’s responses will be. A high creativity setting makes the responses more random and diverse. But it also increases the chance of incorrect or nonsensical outputs. A low creativity setting makes the AI’s answers more predictable and fact-based. But the responses might be too basic.
• Response Length Setting: This controls the approximate length of the AI’s responses. Longer responses allow the AI to provide more detailed explanations. But it limits how much background context you can provide in your prompt. Shorter response settings enable you to give more context upfront in your prompt. But, the AI’s answers may lack depth.

Using moderate creativity settings and medium response lengths is a good starting point. As you get more experience, you can refine these settings per use case. The key is balancing detail, consistency, and context to get optimal results.

Want to learn more about how you can unlock the true potential of AI systems (including advanced prompting techniques)? Download our full eBook entitled The Legal Professional’s Handbook: Generative AI Fundamentals, Prompting, and Applications.

Imagine having a super-powered contract review assistant, able to rapidly comb through thousands of pages in record time to flag key clauses, risks, and insights. That’s the promise of Legal LLMs, generative AI large language models: a highly advanced predictive text system with specialized training in a legal context. For in-house legal teams, these tools accelerate the review of contracts, invoices, and legal service requests by eliminating attorneys needing to pore through mountains of paperwork and emails manually. That’s why AI adoption is surging for these document-intensive tasks that frequently overwhelm in-house legal professionals.

Artificial Intelligence (AI) broadly refers to computer systems capable of tasks requiring human intelligence like visual perception, speech recognition, and decision-making. Machine learning is a specific subfield within AI where algorithms improve through experience without explicit programming. Rather, the AI is trained use a representative dataset. The neural network is a common machine learning structure, inspired by the human brain’s interconnectivity.

A significant AI area utilizing machine learning is Natural Language Processing (NLP), which focuses on automating language understanding and generation. NLP employs neural networks trained on vast text data. Generative AI represents an advanced subset of NLP models called Large Language Models (LLMs) designed to produce human-like text. So, while not all AI uses machine learning, modern innovations like large language models leverage machine learning and neural networks to achieve their natural language capabilities.

This brings us to recent advancements in generative AI and the advent of Large Language Measures (LLMs), which have driven much of the recent excitement around AI applications in the legal field. These are specialized neural networks trained on vast amounts of text data, designed to understand and generate text.

What are Large Language Models?

Large language models (LLMs) like ChatGPT are trained on massive datasets of billions of data points, refined through human feedback loops of prompts and responses. This allows LLMs to break down text into tokens — commonly occurring groups of 4-5 characters – that are encoded as parameters. When you provide a prompt, the LLM uses that context to statistically predict the most likely sequence of tokens to generate a coherent response, like an advanced autocorrect.

However, LLMs have limitations. They don’t learn or understand content — they generate plausible responses using their parameters but don’t comprehend meaning. LLMs have restricted context windows, limiting how much text they can process, require substantial computational resources, and struggle with math or numbers. Poor data quality or biased prompts can result in inaccurate outputs. While LLMs can produce human-like text, they don’t innately understand language semantics. LLMs are powerful but require thoughtful prompts and oversight to mitigate risks. Setting realistic expectations by understanding how they leverage statistical patterns rather than true comprehension allows appropriate usage for augmenting legal work while providing necessary guidance and validation.

Challenges and Common Issues with (Legal) LLMs

While large language models represent a breakthrough innovation, they have inherent limitations requiring prudent risk management. As static systems, LLMs cannot continuously adapt on the fly post-training. Their memory capacity, or “context windows,” vary widely. More limited windows constrain the processing of lengthy content. State-of-the-art models boast expansive context but are still pale compared to human memory.

More concerningly, LLMs have several key issues that warrant caution:

• Hallucinations: LLMs may generate or “hallucinate” data not present in reality, as they are optimized to respond to prompts without the ability to discern truth from fiction. This tendency to produce false information, incredibly confidently stated, is concerning and requires oversight.
• Biases: The training data may contain societal biases encoded into the LLM’s parameters. Additionally, reinforcement learning through human feedback loops during training can further ingrain biases. Once deployed, even prompt wording can introduce biases that lead to unfair LLM responses.
• Inconsistency: Due to the statistical nature of how LLMs generate each token and the inherent randomness built into models to enable creative responses, LLMs do not always take the same path to respond to identical prompts. So, you cannot rely on consistent output, even adjusting for creativity settings.
• Misalignment: LLMs have demonstrated some awareness of when their outputs are being evaluated or tested and can provide responses that diverge from a user’s true intent. This makes it challenging to understand alignment with user goals outside of testing scenarios thoroughly.

Informed perspectives on LLMs’ capabilities and limitations allow full utilization of their transformative potential through responsible oversight. Their breakthrough innovation warrants measured adoption to realize possibilities ethically.

Realizing the Benefits of Legal LLMs & Generative AI While Mitigating the Risks

Generative AI has huge potential upsides for legal teams if thoughtfully applied. But we need to be realistic — Legal LLMs aren’t going to completely replace your skills and judgment overnight. Rather, they can take the grunt work off your plate so you can focus on high-value tasks like strategy, analysis, and client needs.

Before turning LLMs loose, comprehensive testing and review by real experts is crucial. We can’t just immediately take what LLMs spit out as gospel truth. Their output needs real validation via ongoing review. LLMs should collaborate alongside professionals, not try to substitute your judgment that’s sharpened through experience.

It’s also critical to regularly audit for biases, inconsistencies, or false info. The teams behind LLMs must take responsibility for thoughtfully addressing these risks head-on. Rigorous data governance, privacy protection, and cybersecurity are essentials, too. We need systems we can understand, not opaque “black boxes” that undermine trust.

LLMs can uniquely supercharge vital legal work:

• They can rapidly pinpoint the most relevant info for document review out of massive document troves, saving tons of time over lawyers pouring over everything manually. But human oversight still matters to double-check what the LLM flags and catch subtleties it might miss.
• For analyzing contracts, LLMs can efficiently unpack dense legalese to surface issues like inconsistencies or missing pieces for tightening before signing. But niche clauses unique to certain deals might get overlooked. Experts still need to verify that nothing big slipped through the cracks.
• LLMs shine at legal research, promptly finding past precedents, citations, and case law to build persuasive arguments. However, they might miss seminal cases only seasoned attorneys would know; your guidance remains key for strategy.
• LLMs can also assist organizations in the creation of legal service requests and invoice summaries, helping to ensure a more streamlined workflow, saving valuable time, and bringing clarity to collection processes. Human oversight, however, is still essential to ensure crucial elements are included and that requests and summaries get to the right people or departments.

Navigating the Ethical Frontier

Implementing new technologies for a legal team requires prudence to uphold core values like transparency, fairness, and accountability, considering the potential risks and rewards tied to distinct AI models.

While AI promises benefits like efficiency and insights, particularly in routine tasks like contract review, it is imperative to distinguish between consumer models and enterprise solutions of generative AI. Consumer models, like ChatGPT through OpenAI, a version provided through Microsoft, and others provided through Google, are accessible but pose significant data privacy concerns that are unacceptable for legal professionals. Such models may use confidential client data for future training or other purposes, potentially exposing sensitive information inadvertently.

In stark contrast, enterprise solutions offer robust data protection essential for in-house teams. These commercial models assure that client data won’t be used in future model training, nor will the results be shared or misused. This safeguard is pivotal for in-house legal professionals who handle confidential information daily and must assure clients and internal stakeholders about data security. Hence, in-house legal teams should avoid using consumer-level AI models to prevent compromise on client data privacy.

With these distinctions in mind, in-house legal teams must consider the following when evaluating AI solutions for integration into workflows like contract review and legal invoice examination:

• Explainability: In-house legal professionals should require AI providers to disclose the inner workings of their systems. Understanding how recommendations are generated is crucial to fostering trust in AI outputs and preventing reliance on opaque “black box” systems unsuitable for legal work.
• Accountability: Despite AI’s efficiency in reviewing contracts and invoices, in-house lawyers must still thoroughly vet AI outputs, establishing clear oversight procedures without mindlessly following AI-generated advice. Human oversight remains essential.
• Fairness: Ensuring AI is developed without biases is essential to uphold legal principles. Continuous monitoring and assessment during both the development and production phases are necessary to sustain fairness.
• Transparency: In-house teams need to be transparent about their AI usage with clients and courts, clearly communicating the chosen AI’s capabilities and limitations.
• Risk Assessment: Identify and mitigate potential harms, like biases, security flaws, or loss of professional judgment, early when assessing AI solutions for integration into workflows.

The sweet spot is thoughtfully harnessing AI’s power while mitigating risks through governance, security, testing, and expertise-based oversight. This balanced approach lets us ethically integrate AI into legal work to augment your talent.

Ready to learn more about how you can integrate AI into your Legal workflows? Download our full eBook entitled The Legal Professional’s Handbook: Generative AI Fundamentals, Prompting, and Applications.