How to Leverage AI for Legal Research

Legal research is one of the leading areas where in-house lawyers currently use generative AI at work. Surveys in 2024 and early 2025 found legal research to be the second most popular use case among legal professionals at large, and third most popular for in-house counsel. A mid-2025 survey from a different provider found research to be the top legal use case overall. This article will explore how you can start or accelerate your use of generative AI for legal research. 

Benefits of Generative AI for Legal Research

Generative AI is well-suited to legal research for a number of reasons including the technology’s ability to: rapidly digest large volumes of data and documents, create summaries, flag inconsistencies, search for outcomes, calculate analytics, and identify related sources. Legal research consumes an estimated twenty percent of the average lawyer’s time. AI-enhanced legal research can reduce the amount of research time from an estimated baseline of twenty hours to approximately four hours for the average litigation matter. 

Legal Research Use Cases 

Here are some of the most common use cases where generative AI helps in-house lawyers with legal research: 

• Litigation: Document review as part of discovery, case law searches, creating analytics on a specific judge’s prior ruling patterns, and reviewing the litigation history of a specific company or law firm

• Transactional: Reviewing and summarizing documents in a data room for due diligence, evaluating financial data and market trends

• Regulatory: Summarizing case law and regulations, comparing rules by state or over time, generating fifty-state analyses

• Corporate: Analyzing large sets of contracts for specific provisions, inconsistencies, or non-compliance

Tactics for Legal Research Prompts

Here are three actionable tactics for your next AI-assisted legal research project.

Step One: Choose the Right Tool

Before crafting prompts, the first step is to find the AI tool that will best support the task you need to do. Public tools like ChatGPT or Claude can do a passing job at basic research and web searches, but for more precision, explore whether a customized tool might meet your company’s needs. For litigators, consider a specialized tool with docket and court opinion searches. For transactional attorneys, legal-specific AI models may offer greater confidentiality, personas with relevant legal experience, and stronger legal reasoning.   

Step Two: Set up the Prompt

Even with the optimal tool, the output generated is often only as good as the prompt used. These are some tactics our members identified to improve your legal research prompting:

• Give the model a role to play, as much context as possible, and specific parameters on the task and output

• Request outputs structured in IRAC reasoning to better understand the model’s logic and where it may have gone off-course

• Ask for specific source links, and proactively define what constitutes a good source (e.g., authoritative primary and secondary sources)

• If your model does not have a prompt generator, spend some time asking the model for techniques to improve the prompt

• Continue iterating on your given prompt by tweaking the focus and scope based on the outputs generated

Eager to learn more about prompting? Check out The L Suite’s Prompting Guide, as well as our exclusive, members-only library of real-world prompts. 

Step Three: Check for Errors and Manage Risk

While products do exist to check citations in AI-generated output, including court citations, ultimately it is the lawyer’s responsibility to review and evaluate AI legal research before relying on it. Examples abound of attorneys using AI in legal research with insufficient review and analysis over its outputs, which led to fines, job loss, as well as adverse court decisions and business impact. Here is how you can improve your ability to check a given AI’s output:

• Ask the AI for output that can be easily falsified so you can quickly verify whether a particular claim is accurate

• Prompt AI to assume the role of opposing counsel and identify the biggest weaknesses in a particular argument

• Cross-check between different AI models by entering one model’s output into a second model and asking the second model to critique what the first one generated

• Verify citations manually and consider augmenting that process with a citation checker

Compliance and Responsible Use

AI can move quickly and create tremendous efficiency in legal research, but it is also important to remain mindful of the risks that are involved when generative AI is part of the legal research workflow. These risks include those at the model level, like hallucination, errors, and bias, as well as those that are more legal in nature, like confidentiality and intellectual property. 

Here are some compliance best practices for conducting legal research with AI:

• Protect confidentiality by being selective in what information is shared with AI and having enterprise-level contracts in place with vendors that specify how they can use any information being shared

• Choose tools that prioritize confidentiality, such as legal-specific vendors that anonymize data you enter into the model

• Create internal guidelines that help your team identify which information can be shared in prompts, the preferred process for adding a new tool, and high risk outputs that require human review

• Document which models are being used and how, as well as any specific guardrails that may be in place to manage bias and errors, and who is accountable when things go wrong 

For more tips, review our guide to AI compliance, and our Playbook for using Generative AI.

Final Thoughts

Legal research is one of the most popular generative AI use cases for in-house lawyers, and for good reason: it has already delivered on the promise of significant time savings while delivering powerful results for both litigators and corporate lawyers. 

Unleashing AI’s potential for your team begins with selecting a suitable tool for the task at hand, developing thoughtful prompts in an iterative fashion, and finally evaluating the output critically through a mix of technological and manual processes. Through this process, AI-enhanced lawyers can take advantage of the tool’s ability to ingest and analyze tremendous amounts of information very quickly, leading to powerful legal analysis in a fraction of the time.