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How LLMs and Generative AI Can Transform Legal Services

By Nasir Ali

LLMs and generative AI are revolutionizing text generation and comprehension, and the legal industry is feeling their impact. Understanding the pros and cons of competing products.

LLMs and Generative AI for law firms

Large language models and generative AI are revolutionizing text generation and comprehension, and the legal sector is feeling their impact. LLMs, with their ability to process and generate human-quality text, are rapidly transforming how e-discovery, contracts and other legal services are delivered by significantly increasing the pace at which amounts of text, information and data can be analyzed and processed.

While there are many LLMs, such as OpenAI’s ChatGPT, Google’s Gemini, Facebook’s Llama, Mistral and Falcon, some are proprietary and available only from their publisher. Others are available through open-source licenses that need to have their models hosted on cloud providers like AWS or Azure to work. Leading legal technology providers are already offering multiple products that leverage such LLMs, and the number is only expected to increase in the coming years.

Given the various options, it is important for lawyers and legal service providers to be aware of the pros and cons of the competing LLMs and products based on them. This will help determine which LLMs are better suited for specific applications and can deliver better outcomes keeping considerations such as security, confidentiality and accuracy at the forefront.

Advantages of LLMs

Increasing Efficiency and Reducing Cost

LLMs provide the means for tasks such as document review, drafting and research to be executed significantly faster, allowing law firms and legal departments to execute more tasks quickly and get faster outcomes. This also translates into cost and time savings, which can continue to be significant even after accounting for the cost of the technology being used.

Simplifying Tasks and Automating Workflows

LLMs can help to automate several tasks traditionally performed by paralegals, such as summarization and drafting of basic documents. This can allow legal teams to be restructured to focus on more analytical and creative work alongside AI tools. This will also let lawyers reduce tedious tasks and increase their bandwidth, allowing them to focus on higher-value activities like client strategy, negotiation and courtroom advocacy.

Improving Quality and Accuracy

Since LLMs are not subject to human bias and errors, they can improve the accuracy of tasks that are more transactional and require less context. For more complicated tasks, such as reviewing documents for investigations or vetting clauses, LLMs can also be used to validate the work product for consistency and flag documents for QC or secondary review, helping improve quality.

Multiple Applications

LLMs have the capacity to analyze vast information quickly, which makes them well-suited to various legal applications such as:

  • Identifying key facts and relevance from documents subject to discovery.
  • Summarizing documents, depositions and hearing transcripts.
  • Classifying and analyzing groups of documents.
  • Identifying relevant cases, statutes and expert opinions in legal databases.
  • Creating first drafts of contracts, initial pleadings and other legal documents.

Considerations for Choosing LLMs and Generative AI

Limited Context Windows

A context window in generative AI represents the amount of textual information that the AI or underlying LLM can take into consideration at any given time when processing language. This is like a human lawyer reading a contract who can only recall and use a certain portion of the clauses when making a decision. The capacity or context window of an LLM is generally denoted by tokens and can be a crucial factor in picking the best LLM/genAI technology for a particular task.

Limited Knowledge Portability

Because of the nature of context windows, most LLMs have very limited portability of knowledge. For example, while reviewing multiple documents pertaining to a specific set of instructions, most LLMs would only be able to review each document individually based on the provided instructions but may not be able to carry any additional knowledge from one document in the set to another like a human lawyer could.

Data Security

Data security is a key factor in choosing an LLM for legal applications, depending on the nature of the information it will be processing. Any LLM used on confidential, sensitive or privileged data must be licensed and include nondisclosure and nonuse provisions to ensure privilege is not waived and confidentiality is not compromised.


Several recent instances have shown that generative AI and underlying LLMs may be prone to generating erroneous results due to unvalidated information provided as input or other technological limitations. This is sometimes referred as “hallucination” and can be a risk factor depending on the application. Hence, it is important that any output from LLMs being used for applications such as litigation, due diligence and contracting is comprehensively vetted and cross-verified to mitigate legal risk and sanctions.

Tools That Add to Your Capabilities

LLMs and generative AI are not meant to be replacements for lawyers and paralegals but can add to their capabilities, transforming the way legal services are delivered. There is a greater need for collaboration between technology providers and lawyers to overcome the technical limitations and ethical concerns of using LLMs and to train and upskill teams to use them effectively.

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Nasir Ali

Nasir Ali, CEDS, is Assistant Vice President of Litigation and Investigation Services at Proxiio, a breakthrough provider of legal process outsourcing and technology solutions. Nasir oversees project intake and execution for major client engagements. With more than 13 years of experience in e-discovery, litigation support, and document review for global clients he also consults with clients to create advanced review workflows, leveraging TAR, generative AI and other analytics tools.

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