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In recent years, Generative AI has emerged as a transformative technology capable of generating various forms of content, including text, images, and music, based on its training data.
This innovation could revolutionize in-house legal's day-to-day work to make legal services more efficient and effective. According to the 2023 Wolters Kluwer’s Future Ready Lawyer Survey, 73% of lawyers expect to leverage generative AI tools in their legal work within the next year.
While Generative AI can be a valuable tool for legal professionals, it should be used judiciously, with the oversight and expertise of legal experts.
In this blog post, we examine what in-house lawyers and legal professionals should know about Generative AI and large language models (LLMs), the benefits they bring to in-house legal teams, and the associated risks and limitations (and how to manage them).
Generative AI (sometimes called GenAI) is AI technology that can generate new content, ranging from text to images and even music, based on the data it has been trained on. This technology is revolutionary in its ability to create original, realistic outputs that can mimic human creativity.
Generative AI can be a game-changer. It can draft legal documents, create legal briefs, and even generate detailed reports, saving time and resources for legal professionals. However, it’s important to note that while Generative AI can produce content, the oversight and expertise of legal professionals are crucial to ensure accuracy and relevance to specific legal contexts.
The LLM you're more familiar with as an attorney is probably a Master of Laws degree (pun intended). In the context of AI, LLMs are sophisticated machine learning models designed to understand and generate human language at scale.
These models process vast text datasets to learn patterns in how words, sentences, and legal concepts interact. For in-house counsel managing high-volume contract reviews or complex compliance questions, understanding which LLM is best for legal documents is crucial for operational efficiency.
Legal AI LLM systems differ significantly from general-purpose models. While consumer AI tools train on broad internet data, legal-specific models focus on case law, statutes, regulations, and legal commentary. This specialization matters where precision is critical.
The practical impact for legal teams is substantial. Instead of spending hours researching precedents or reviewing standard clauses, attorneys can use properly trained LLMs to accelerate initial drafts, identify potential issues, and maintain consistency across legal documents.
However, the key lies in selecting models that understand legal matters rather than simply processing text.
Prompt engineering is the art of communicating effectively with AI systems to generate accurate, relevant legal outputs. For attorneys learning how to use AI for legal work, mastering this skill can dramatically improve the quality and usefulness of AI-generated content.
The process involves crafting detailed queries that guide the LLM toward precise responses. Rather than asking generic questions, successful prompt engineering requires legal professionals to frame requests with the same specificity they would use when instructing a junior associate.
Consider the difference between these two approaches:
Generic prompt: "What are the implications of a breach of contract?"
Engineered prompt: "What are the potential legal consequences under New York law for a material breach of a commercial lease agreement where the tenant has failed to maintain required insurance coverage?"
The engineered prompt specifies jurisdiction, contract type, breach nature, and specific obligation. This precision helps the LLM focus on relevant legal precedents and statutory requirements, delivering actionable guidance rather than broad generalities.
Effective prompt engineering is a force multiplier for in-house teams managing diverse legal requests. When reviewing generative AI for legal contracts, properly crafted prompts can help identify missing clauses, suggest risk mitigation language, and ensure consistency with company policies and risk tolerance.
Fine-tuning transforms general-purpose AI models into specialized legal tools by training them on curated legal datasets. This process addresses a fundamental weakness that generic LLMs face. Though powerful, they lack the nuanced understanding required for high-stakes legal work.
The fine-tuning process involves feeding the model specialized legal content: case law from relevant jurisdictions, industry-specific contracts, regulatory guidance, and your legal department's own playbooks and precedents. This additional training helps the model understand legal context, terminology, and reasoning patterns specific to your practice areas.
For example, when implementing generative AI for legal contracts, a fine-tuned model trained on software licensing agreements will better understand concepts like intellectual property indemnification, service level obligations, and limitation of liability clauses. The model learns the meaning of these terms and how they interact within the broader contract structure.
The business impact becomes clear when you consider contract review efficiency. A fine-tuned model can quickly identify non-standard terms, flag potential risk areas, and suggest language that aligns with your company's established positions.
Instead of starting each contract review from scratch, attorneys can focus on strategic decision-making while the AI handles initial analyses and drafting suggestions.
However, fine-tuning requires ongoing investment in data curation and model maintenance. Legal language evolves, regulations change, and your company's risk appetite may shift. The most effective implementations combine fine-tuned models with robust validation processes and regular updates to training datasets.
Using the right LLM can offer several benefits to in-house legal departments, including improving efficiency, accuracy, and overall quality of legal work. LLMs can significantly transform the workflows of in-house legal teams by automating routine tasks, providing quick access to information, and assisting in complex legal analysis.
The time and effort savings enable lawyers to focus on more strategic and high-value aspects of their work.
LLMs can swiftly sift through vast amounts of legal data, including case law, statutes, and legal journals, to provide concise summaries or answers to specific legal queries.
For instance, if an in-house lawyer needs to understand the nuances of intellectual property law in a specific jurisdiction, the LLM can quickly pull relevant information in minutes, saving hours of manual research.
LLMs can be programmed to read, understand, and automatically categorize incoming legal requests based on extracted key fields or terms. This legal front door automated categorization and workflow routing is critical in managing the flow of legal requests, ensuring they are promptly directed to the appropriate contact on the legal team.
For example, when an LLM receives a request related to a sales contract, it can analyze the request or attached contract to extract key information such as contract value, involved parties, or urgency.
The legal department can set predetermined criteria for routing to ensure that each request is reviewed and approved by the right contact.
The legal team can also set the tool to automatically flag and route sales contracts valued at over $100,000 to a specific team member who handles high-value contracts and/or the finance team for further review and approval.
LLMs can assist in drafting various legal documents such as contracts, agreements, and memos. They can suggest language, format documents according to legal standards and even flag potential legal issues. For example, when drafting a non-disclosure agreement, an LLM can suggest standard clauses and tailor them to specific needs, ensuring compliance and efficiency.
In-house legal teams often deal with a high volume of contracts. LLMs can automate parts of the contract review process, identify key clauses, and flag and highlight areas of risk or non-compliance. This capability is useful in due diligence processes where quickly reviewing numerous documents is crucial.
LLMs can monitor changes in laws and regulations and alert the legal team about relevant updates. For example, if new data protection regulations are introduced in the European Union, the LLM can summarize these changes and suggest how they might impact the company’s operations.
LLMs can provide basic legal guidance and training to employees in other departments, helping them understand compliance requirements, contractual obligations, and company policies. These LLMs can serve as a dynamic knowledge management tool and reduce the legal team’s workload in addressing routine or repetitive queries.
Advanced LLMs can analyze past legal cases and outcomes to predict potential risks and consequences of legal decisions or strategies. For instance, before pursuing litigation, an LLM can analyze similar cases and their outcomes, aiding in strategic decision-making.
LLMs can be fine-tuned to a legal department's specific needs and language, increasing the relevance and accuracy of their outputs.
Legal teams are finding practical applications for AI across their daily workflows. These use cases deliver measurable efficiency gains while maintaining quality standards.
AI can identify non-standard clauses, extract key terms, and flag potential risks in vendor agreements, employment contracts, and commercial deals. These tools can also compare contract language against your company's standard positions and highlight deviations for attorney review.
Example: When reviewing a software licensing agreement, AI flags that the liability cap is set at $50,000 instead of the standard $500,000 minimum and identifies an unusual data retention clause that requires 10-year storage instead of the 3-year requirement.
AI accelerates initial research by quickly analyzing case law, statutes, and regulations relevant to specific legal questions. The technology helps attorneys identify relevant precedents and build stronger legal arguments while reducing research time from hours to minutes.
Example: An employment dispute arises regarding remote work policies. AI quickly identifies relevant cases involving similar policy changes in your jurisdiction, highlights key court decisions on employee accommodation requirements, and summarizes how recent legislation affects your company's position.
Generate first drafts of common legal documents like NDAs, employment agreements, and policy updates using AI trained on your department's preferred language and structure. This approach maintains consistency while freeing attorneys for higher-value work.
Example: HR requests a new social media policy for employees. AI generates a first draft incorporating your company's existing policy framework, industry best practices, and relevant state employment laws, reducing drafting time from 4 hours to 30 minutes of review and customization.
AI can rapidly review large document sets during M&A transactions or compliance audits, categorize documents, identify potential issues, and create summary reports. This capability is particularly valuable for time-sensitive deals with extensive documentation.
Example: During acquisition due diligence, AI reviews 10,000 contracts in 48 hours, categorizing them by type, identifying 23 contracts with change-of-control provisions, flagging 8 agreements with problematic termination clauses, and creating an executive summary for review.
Track regulatory changes across multiple jurisdictions and assess their impact on business operations. AI can monitor regulatory updates, analyze new requirements, and alert legal teams to changes affecting their industry or business model.
Example: New data privacy regulations are proposed in California. AI analyzes the draft legislation, compares it to your current privacy practices, identifies 5 policy gaps that need addressing, and generates a compliance timeline with recommended actions.
Automate routine legal workflows like intake questionnaires, matter creation, and status updates. AI can route requests to appropriate team members, generate standard responses, and maintain audit trails for legal project management.
Example: A sales team submits a contract review request through your intake system. AI automatically categorizes it as a "standard vendor agreement," routes it to the appropriate contract attorney, creates a matter in your case management system, and sends status updates to stakeholders throughout the review process.
Analyze contract terms, business decisions, and operational changes to predict potential legal risks. AI can score contracts based on risk factors, identify compliance gaps, and suggest mitigation strategies based on historical data and legal precedents.
Example: Your company plans to expand into a new market. AI analyzes similar expansions, identifies potential regulatory hurdles, predicts likely compliance costs based on comparable companies, and recommends a risk mitigation strategy including necessary licensing and local counsel requirements.
As Generative AI and LLMs continue to evolve, their potential applications in law are vast. From automating routine tasks to providing analytical insights, these technologies could redefine legal work. However, lawyers must understand the scope of AI capabilities and their ethical usage while maintaining human oversight.
A critical aspect of Generative AI for legal professionals is AI hallucinations. Hallucination refers to instances where AI systems generate factually incorrect or nonsensical information despite appearing plausible or coherent.
AI hallucinations occur when AI systems generate output based on patterns learned from the training data rather than factual accuracy. These outputs can be misleading because they are often presented with confidence that belies their unreliability.
An AI tool designed to provide legal advice might “hallucinate” by confidently presenting legal precedents or incorrect interpretations of the law. Generative AI tools have been known to confidently cite case law that doesn’t exist.
Case in point: In 2023, Roberto Mata sued Avianca Airlines, claiming a metal serving cart injured him during a flight. Avianca asked to dismiss the lawsuit based on the statute of limitations. Mata’s lawyer, Steven A. Schwartz, an experienced New York attorney who had practiced for three decades, used ChatGPT to perform legal research for his brief.
He submitted a brief citing more than half a dozen relevant court decisions that ChatGPT gave him, including Martinez v. Delta Air Lines, Zicherman v. Korean Air Lines, and Varghese v. China Southern Airlines.
However, the airline’s lawyers and the judge could not find the decisions or case quotes cited, because they didn’t exist. ChatGPT had hallucinated the cases! When Schwartz repeatedly asked ChatGPT to confirm its output, ChatGPT confidently replied, “No, the other cases I provided are real and can be found in reputable legal databases such as LexisNexis and Westlaw.”
Schwartz admitted to unknowingly using these false AI-generated citations in his legal arguments, leading to sanctions by the judge. The case highlights the challenges and risks of depending on ChatGPT for legal research and emphasizes the need for caution and verification.
Legal professionals face unique ethical obligations when using AI tools. Knowing these risks helps you develop appropriate safeguards and maintain professional standards.
Not all AI tools are, or should be, treated equally. General-purpose LLMs like OpenAI's ChatGPT, Google's PaLM 2, and Meta's Llama 2 are not specifically built or designed for legal work, and this can lead to significant inaccuracies.
Researchers tested these popular models with over 200,000 legal questions and found that they hallucinated at least 75% of the time when answering questions about court rulings.
In one test, the researchers asked the models whether two court rulings agreed with each other. The general-purpose models performed no better than random guessing. This high error rate underscores the need for AI tools that are specifically developed and fine-tuned for specific legal use cases.
Although the whole idea behind AI is to do things faster or more efficiently, it's crucial to ensure in-house legal work is accurate and dependable, especially when a small mistake could have an outsized impact.
There's no universal answer, but here's what works well for different needs.
For contract analysis, legal-specific platforms like Harvey AI, LegalOn, and ThoughtRiver have built-in understanding of legal language and risk assessment. These tools provide clause extraction and redlining suggestions without extensive prompt engineering.
General-purpose models like GPT-5, Claude, and Gemini excel at legal research, memo drafting, and case analysis when properly prompted. Many legal departments use these with strong validation processes for initial research and document creation.
Security-focused organizations often choose Microsoft Copilot for legal professionals or LexisNexis+ for enterprise-grade protection within familiar platforms. On-premises solutions work best for highly sensitive work.
Modern legal teams combine multiple AI tools rather than relying on one solution. A typical setup might include a legal-specific LLM for contracts, a general model for research, and specialized tools for compliance or patent work.
When evaluating options, prioritize solutions that integrate with your existing systems and offer transparent pricing that scales with your needs.
To mitigate the risks of AI, it’s essential for legal professionals to:
The integration of AI in legal work should be a carefully managed process. To find the right use case and fit, the following steps must be incorporated.
Identify the specific needs and areas where AI can add value. This involves analyzing existing workflows to pinpoint tasks that are time-consuming, prone to human error, or could be optimized through automation, such as legal request intake, document review, legal research, or contract management.
Securing buy-in and executive support is critical to successfully integrating AI tools within in-house legal teams. To do so, create a compelling business case that clearly outlines the benefits of the AI tool.
This involves demonstrating how the tool can improve efficiency, reduce costs, or enhance the quality of legal services. Showcase how it aligns with the organization's broader business objectives and strategies. Quantifiable metrics, such as expected return on investment, cost savings, or time-to-value, can be particularly persuasive.
Having an executive sponsor is critical. An executive sponsor can champion the tool at the highest levels of the organization, ensuring that it receives the necessary resources and attention, from budget to implementation and cross-functional rollout.
Ideally, this sponsor should understand the AI tool's capabilities and the legal team's specific needs and be able to articulate to other executives and stakeholders how the tool meets these needs.
Since some legal AI tools are used by both legal and business stakeholders, having an executive sponsor can help get top-down buy-in, adoption, and usage from partner teams such as sales, marketing, product, and IT.
It’s essential to choose tools that are powerful and align with the legal profession's ethical standards. Some factors to evaluate include the solution's:
Adequate internal support for AI tools is crucial for successful implementation and utilization. This begins with designating a dedicated point of contact for the in-house legal team to oversee the implementation process and ensure smooth integration into existing workflows.
A legal operations manager or counsel might handle the day-to-day coordination and implementation. After implementation, it’s equally important to set up comprehensive training sessions tailored to the team’s needs. This helps foster strong buy-in and commitment to usage.
Active and continuous engagement with the vendor’s customer success manager is also key. Maintaining open lines of communication with them can help address any issues promptly and learn about the latest feature improvements.
Getting continuous feedback from the legal team and providing it to the vendor ensures that the tool evolves per the team’s requirements. Combining internal coordination with proactive vendor engagement is essential to fully leverage the capabilities of AI tools for in-house legal, ensuring they add real value and efficiency to the team’s operations.
While general-purpose AI tools offer broad capabilities, in-house legal teams need solutions designed specifically for their unique workflows and challenges.
Streamline AI combines intelligent intake automation with AI-powered contract analysis, giving legal departments specialized tools to manage high-volume requests efficiently.
Our platform integrates seamlessly with your existing legal technology stack, automatically routing legal requests, extracting key contract terms, and maintaining the audit trails and security standards that legal professionals require.
Ready to see how purpose-built legal AI can transform your department's efficiency? Schedule a demo to learn how Streamline AI helps legal teams reduce manual work while maintaining the accuracy and oversight that legal work demands.
Always verify AI-generated citations through trusted legal databases like Westlaw or LexisNexis before using them. Implement mandatory validation where qualified legal professionals cross-check all AI research outputs.
Require enterprise-grade encryption, data residency controls, and agreements that client data won't be used for training. Look for SOC 2 compliance and attorney-client privilege protections.
Start by identifying existing pain points like repetitive contract reviews, then show how AI addresses these challenges. Provide comprehensive training that emphasizes AI as an enhancement, not a replacement.
Begin with high-volume, routine tasks: contract review, legal request intake automation, and initial research for common questions. These show immediate time savings before moving to complex applications.
Scale your legal team's efficiency and effectiveness with modern workflow automation tools designed for in-house legal.
