The generative AI-driven solution enables UK legal professionals to find the best starting point from their own databases or Practical Law templates, use content from Practical Law alongside their legal department鈥檚 or firm鈥檚 contract repository to draft or revise clauses, leverage Practical Law contract playbooks, and correct common drafting errors. It uses AI to refine and review documents, producing more accurate, higher-quality work 鈥� without leaving Microsoft Word.

I recently shared a CoCounsel Drafting demo with 鈥檚 Richard Tromans.

鈥淭he offering has been especially shaped for legal needs here and will directly engage with TR鈥檚 contract data in Practical Law,鈥� Tromans said in .

I highlighted CoCounsel Drafting capabilities and features for Tromans, and we discussed how 鈥淟LM-supported drafting could be truly transformative.鈥� We talked about how users will be able to use the large language model (LLM) to modify a clause, for example, and how CoCounsel Drafting uses generative AI grounded in retrieval augmented generation (RAG) to access聽Practical Law content and notes.

We also discussed how CoCounsel Drafting delivers a level of detail similar to what a junior associate may produce when asked to draft a contract. I noted that CoCounsel Drafting allows customers to use their own playbook to draft a preferred set of terms for any contract type or use a Practical Law playbook for a range of popular contract types.

Finally, Tromans and I discussed integrations 鈥� with HighQ and with document management systems (DMS) including iManage 鈥� as well as security.

On the other side of the pond, U.S. customer feedback on CoCounsel Drafting, which we introduced earlier this year, has been overwhelmingly positive. I hope that UK legal professionals are equally pleased with the time savings and productivity increases CoCounsel Drafting offers.

As a former lawyer turned AI product leader, I’ve lived the pain of tedious legal tasks firsthand. That’s why I’m passionate about harnessing generative AI to transform legal drafting. I know how much time and energy gets wasted on repetitive tasks, taking away from the work that really matters. Now, I’m excited to help lawyers reclaim that time and focus on what they love 鈥� whether that’s helping clients, developing strategy, or simply enjoying a better work-life balance.

You can learn more about CoCounsel Drafting , and check out Tromans鈥� blog .

This is a guest post from Rawia Ashraf, vice president of Product Management, 成人VR视频.

First, let鈥檚 settle on terminology. We should use terms like 鈥渆rrors鈥� or 鈥渋naccuracies鈥� instead of 鈥渉allucinations.鈥� 鈥淗allucination鈥� sounds smart, like we鈥檙e AI insiders and know the lingo, but the term is often defined narrowly as a fabrication, which is just one type of error. Customers will be as concerned, if not more concerned, about non-fabricated statements from non-fabricated cases that, despite being real, are still incorrect for the question. 鈥淓rrors鈥� or 鈥渋naccuracies鈥� are much better and more encompassing ways to describe the full range of problems we care about.

Next, let鈥檚 consider types of errors and risk of harm from each. Error rates are often just reported as a percentage, which is a binary view 鈥� either an answer has an error or it does not, but that鈥檚 overly simplistic. It conflates the big differences in risk of harm from different types of errors and ignores the potential benefit of lengthy and nuanced answers that contain a minor error.

There are dozens of ways to categorize errors in LLM-generated answers, but we鈥檝e found three to be most helpful:

1. Incorrect references in otherwise correct answers
2. Incorrect statements in otherwise correct answers
3. Answers that are entirely incorrect

A fourth category of error that sometimes comes up in discussions with customers is about inconsistency, where the system provides a correct answer one time, then later, when the same exact question is submitted, the answer is different and sometimes less complete or incorrect. Minor differences in wording are very common when submitting the same question. Substantial differences are uncommon, but when they do result in an error, the error simply falls into one of the three categories above.

Incorrect references refer to situations where an answer is correct, but the footnote references provided for a statement of law does not stand for the precise proposition of the statement. Fortunately, risk of harm with these types of errors appears to be low, since they鈥檙e easy to detect when researchers review the primary law cited. Answers with these types of errors still offer substantial benefit to researchers because they get them to the right answer quickly, often with a lot of nuance about the issues, but the researcher still has to use additional searches or other research techniques to find the best source material.

Incorrect statements in otherwise correct answers are often obvious in the answer. An answer might say the law is X in paragraphs 1 鈥� 4 and then, inexplicably, declare the law is Y in paragraph 5, then go back to stating the law is X in paragraph 6. Risk of harm with these errors also appears to be low, since the inconsistency is obvious and prompts the researcher to dig into the primary law to figure it out. Answers with these types of errors still offer some benefit, since they point the user to highly relevant primary law, explain the issues, and help the researcher with what to look for when reviewing primary law.

Answers that are entirely wrong are more problematic. These are quite rare in our testing, but they do occur. Often a simple check of the primary sources cited will resolve the error quickly, but sometimes additional research is needed beyond that. These answers still offer some benefit to researchers, since they often point to relevant primary law in a way that is more effective and useful than traditional searching, but they also come with greater risk of harm, since the incorrectness of the answer is not obvious, and simply reviewing cited sources does not always resolve the issue.

These sound scary, but researchers have been dealing with this type of issue for ages. For instance, secondary sources can be incredibly helpful for summarizing complex areas of law and offering insights, but they sometimes fail to discuss important nuance, and sometimes the law has changed since they were written. If researchers relied on them alone, without doing further research, they would be at risk of harm, even if they consulted cited primary sources.

Yet we would never tell researchers to avoid using secondary sources because they can sometimes be beautifully written, very convincing, and utterly wrong. What we tell researchers is they can be enormously helpful for research but must be used as part of a sound research process where primary law is reviewed, and tools like KeyCite, Key Numbers, and statutes annotations are used to make sure the researcher has a complete understanding of the law.

Individual research tools have rarely been perfect. Their value has been in improving sound research practices. Stephen Embry captured this idea well in his recent blog post, :

鈥淭he point is not whether Gen AI can provide perfect answers. It鈥檚 whether, given the speed and efficiency of using the tools and their error rates compared to those of humans, we can develop mitigation strategies that reduce errors. That鈥檚 what we do with humans. (I.E. read the cases before you cite them, please).鈥�

But if you must check primary resources and engage in sound research practices when using a research tool, is there really any benefit to using it? If it improves overall research times or helps surface important nuance that might otherwise be missed, the answer is yes.

Prior to launching AI-Assisted Research, we knew large language models would not produce answers free of errors 100% of the time, so we asked attorneys if the tool would be valuable even with an occasional error, and if we should we release it now or wait until it was perfect?

Most of the attorneys said, 鈥淚 want this now.鈥� They saw clear benefits and thought an occasional error was worth it for the extraordinary benefits of the new tool, since they would easily uncover an error when reading through primary law. They said that if they knew the answers were generated by AI, they would never trust them and would verify by checking primary sources. If there was an error, those primary sources (and further standard research checks, like looking at KeyCite flags, statute annotations, etc.) would reveal it. That鈥檚 why we put AI in the name of this CoCounsel skill, so researchers would be encouraged to check primary sources.

Our customers have submitted over 1.5 million questions to AI-Assisted Research in Westlaw Precision. Generally, three big research benefits come up in discussions:

1. It gives them a helpful overview before diving into primary sources.
2. It uncovers sub-issues, related issues, or other nuances they might not have found as quickly with traditional approaches.
3. It points them to the best primary sources for the question more quickly and efficiently than traditional methods of research.

Customers have described these benefits with great enthusiasm, telling us AI-Assisted Research 鈥渟aves hours鈥� and is a 鈥済ame changer.鈥�

Lawyers know they need to rely on the law when writing a brief or advising a client, and the law lies in primary law documents (cases, statutes, regulations, etc.). Researchers have always known that when they鈥檙e looking at something that is not a primary law document, such as a treatise section, a bar journal article, or an answer from AI, they must check the primary law before relying on it to advise a client or write a brief. That鈥檚 why we cite to primary law in the answers and why we provide an even greater selection of relevant primary and secondary sources under the answers 鈥� to make this checking easy.

But what about ? That lawyer submitted his brief without ever reading any of the cases he was citing.

That can鈥檛 be the standard for considering the value of products like Westlaw that provide a rich set of research tools that make it easy to check primary sources, understand their validity, and find related material. If the standard were, a user might not read any of the primary law, many high-value research capabilities today would be deemed useless.

The way to dramatically reduce the risk of harm from LLM-based results or any other individual research tool, like secondary sources, is what it has always been: sound research practices.

Jean O鈥橤rady conveyed this beautifully in :

鈥淒oes generative AI pose truly unique risks for legal research? In my opinion, there is no risk that could not be completely mitigated by the use of traditional legal research skills. The only real risk is lawyers losing the ability to read, comprehend and synthesize information from primary sources.鈥�

At 成人VR视频, we鈥檙e continuing to work on ways to reduce all types of errors in generative AI results, and we expect rapid improvement in the coming months. Because of the way large language models work, even with retrieval augmented generation, eliminating errors is difficult, and it鈥檚 going to be quite some time before answers are completely free of errors. That鈥檚 the bad news.

The good news is that harm from these types of errors can be reduced dramatically with common research practices. It鈥檚 why we鈥檙e not only investing in generative AI projects. We鈥檙e also continuing to build out a full suite of research tools that help with the entire research process because that process will continue to be important.

Even when errors get reduced to just 1%, that will still mean that 100% of answers need to be checked, and thorough research practices employed.

We鈥檙e currently involved in two consortium efforts to provide benchmarking for generative AI products. When generative AI products for legal research are tested against these benchmarks, I expect we鈥檒l see the following:

• None of the products will produce answers that are all entirely free of errors.
• All the products will require sound research practices, including checking primary law documents, to reduce risk of harm.
• When sound research practices are employed, the risk of harm from errors in the answers is small and no different in magnitude from the risks we see with traditional research tools like secondary sources or Boolean search.

Even in the age of generative AI, sound research practices remain important and are here to stay. As Aravind Srinivas, CEO and cofounder of , said,

鈥淭he journey doesn鈥檛 end once you get an answer鈥� the journey begins after you get an answer.鈥�

I think Aravind鈥檚 statement applies perfectly to legal research and to the art of crafting legal arguments. Even as our teams strive to reduce errors further, we should keep in mind the benefits of generative AI and weigh them against the new and traditional risks of harm in tools that are less than perfect. When used as part of a thorough research process, these new tools offer tremendous benefits with very little risk of harm.

This is a guest post from Mike Dahn, head of Westlaw Product Management, 成人VR视频.

Available now to customers in the United States, this skill helps legal professionals quickly get to answers for complex research questions. This generative AI skill leverages innovation in Casetext, created by taking a 鈥渂est of鈥� approach using the 成人VR视频 Generative AI Platform.

With AI-Assisted Research and聽, attorneys are empowered with eight generative AI-powered core skills, including AI-Assisted Research on Westlaw Precision, Prepare for a Deposition, Draft Correspondence, Search a Database, Review Documents, Summarize a Document, Extract Contract Data, and Contract Policy Compliance. The company also laid out high-level product roadmaps to develop numerous additional generative AI skills to address customer-specific needs. Each additional skill will be built on a common software framework within the 成人VR视频 Generative AI Platform.

AI-Assisted Research allows customers to ask complex legal research questions in natural language and quickly receive synthesized answers, with links to supporting authority from Westlaw content and links to further examine that authority. AI-Assisted Research streamlines the initial phase of legal research with sophisticated answers to questions and the authority those answers are based on, saving hours of work. These responses are founded on more than 150 years of 成人VR视频 classification, analysis, and editorial expertise contributed by subject matter experts and attorney editors.

AI-Assisted Research employs Retrieval Augmented Generation (RAG) to prevent the large language models (LLMs) from making up things like case names and citations by focusing the LLMs on the actual language of Westlaw content. Future plans include expanding generative AI throughout the research process in Westlaw and bringing these capabilities to versions of Westlaw outside the United States.

Also, the company announced that it will be building on the AI assistant experience Casetext created with CoCounsel, the world鈥檚 first AI legal assistant. Later in 2024, 成人VR视频 will launch an AI assistant that will be the interface across 成人VR视频 products with generative AI capabilities.

The AI assistant, called CoCounsel, will be fully integrated with multiple 成人VR视频 legal products, including Westlaw Precision, Practical Law Dynamic Tool Set, Document Intelligence, and HighQ, and will continue to be available on the CoCounsel application as a destination site.

In addition, 成人VR视频 will introduce generative AI within Practical Law Dynamic Tool Set in January 2024. Customers will benefit from generative AI within Practical Law through a new interface with an AI legal assistant, which will quickly provide answers using conversational language 鈥� all validated by trusted Practical Law content created and maintained by a team of more than 650 legal experts.

Read the聽press release聽for more on AI-Assisted Research on Westlaw Precision, the new generative AI assistant connecting all 成人VR视频 generative AI products, 成人VR视频 Generative AI Platform, generative AI capabilities in Practical Law, and CoCounsel Core. For more on how 成人VR视频 is ensuring that its AI products and skills are built responsibly, check out the company鈥檚聽Data and AI Ethics Principles.