From TAR to Generative AI: A Revolution in Document Review

The legal world has long relied on technology-assisted review (TAR) as a trusted tool for e-discovery. For years, TAR helped legal teams manage the overwhelming volume of documents, narrowing down relevant information and streamlining the discovery process. But as the complexity of data grows and the demands for accuracy and efficiency increase, a new contender has entered the scene: generative AI (GenAI). This groundbreaking technology is poised to redefine document review, addressing the gaps TAR struggles to fill.

TAR emerged as a game-changer in e-discovery, leveraging supervised machine learning to classify documents into categories like relevant or irrelevant. Its promise of efficiency revolutionized how legal teams approached discovery, but as the data landscape evolved, so too did the cracks in TAR’s capabilities.

For one, TAR’s effectiveness is limited to text-based documents. It cannot process images, review audio files, or handle documents that lack textual data. Spreadsheets, with their unique structure and embedded formulas, often pose significant challenges. And when it comes to multilingual datasets, TAR frequently stumbles, requiring separate workflows for different languages—a logistical headache for international cases.

The process of training TAR is another sticking point. Typically, TAR requires hours of manual input to identify and label seed sets. This is not only tedious but also prone to human error, as reviewers must painstakingly curate the initial training data. While TAR can achieve recall rates of 70%-80%, these limitations often mean that critical documents slip through the cracks, leaving room for improvement in high-stakes cases.

Generative AI represents a leap forward in document classification, addressing the very shortcomings that hinder TAR. Built on large language models (LLMs), GenAI brings a level of understanding and versatility that traditional TAR simply cannot match. Its ability to process and analyze diverse data types is reshaping what’s possible in e-discovery.

Take recall rates, for example. Where TAR might achieve a respectable 70%-80%, GenAI pushes beyond, consistently achieving 90%+ recall. This dramatic improvement significantly reduces the risk of missing key documents, giving legal teams a higher degree of confidence in their review process.

But recall is just the beginning. Unlike TAR, GenAI can process data beyond text. It can analyze images, identify embedded text, and interpret visual elements. Audio files, long overlooked in traditional workflows, are transcribed and reviewed seamlessly. Even complex spreadsheets are no longer a challenge, as GenAI can understand patterns, formulas, and data structures with ease.

One of GenAI’s most transformative features is its ability to handle multilingual datasets in a single workflow. Gone are the days of segmenting reviews by language or creating separate workflows for international cases. With GenAI, a legal team can process data from around the globe effortlessly, breaking down barriers that once slowed discovery to a crawl.

Perhaps one of the most exciting aspects of GenAI is how it simplifies the training process. Instead of hours of manual labor, GenAI models can be trained in minutes. The process is intuitive, often requiring little more than a small set of labeled data to get started. For legal teams, this means less time spent on setup and more time focused on strategy.

What’s more, GenAI goes beyond identifying relevance. It can pinpoint key documents, apply issue codes, and categorize data with a nuanced understanding of case issues. This adds layers of intelligence to the review process, helping attorneys quickly zero in on the information that matters most.

TAR: Defensibility Through Metrics

Defensibility in TAR workflows is traditionally achieved through the use of metrics that, while valuable, are often abstract and difficult for nontechnical stakeholders to fully grasp. Legal teams are also often expected to provide detailed documentation of the TAR methodology, including the training process, validation techniques, and sampling results. This means defensibility in TAR can hinge on a labor-intensive process of proving that the model was trained and validated correctly.

While these efforts are effective, they can still leave gaps in understanding. TAR’s reliance on metrics alone means it often lacks the ability to explain why specific documents were classified in a certain way or how they relate to broader data patterns.

GenAI: Defensibility Through Insight

Generative AI transforms how defensibility is approached by providing not only metrics but also deep insights into the data itself. This goes beyond traditional recall and precision scores, enabling legal teams to demonstrate defensibility at multiple levels:

• Granular insights into the data: GenAI doesn’t just classify documents—it can summarize their content, explain their relevance, and highlight connections to key issues or themes in the case.
• Multimodal Analysis: Because GenAI can analyze images, audio, and non-textual data, it offers defensibility across all data types.
• Cross-Language and Issue-Level Defensibility: GenAI’s ability to handle multilingual datasets and apply issue codes automatically ensures defensibility even in complex, global cases.
• Speed and Accuracy Metrics: While GenAI still provides traditional metrics like recall and precision, its higher accuracy rates (90%+) reduce the need for extensive justification through numbers alone.

With TAR, legal teams work hard to defend their methodology and outcomes. With GenAI, the process becomes significantly easier. By offering transparency, actionable insights, and unparalleled accuracy, GenAI enables legal teams to demonstrate defensibility on multiple levels. It’s not just about proving the process worked—it’s about showing why it worked and how it uncovered the key facts of the case.

The transition from TAR to GenAI isn’t just a technological upgrade—it’s a paradigm shift. TAR served its purpose well during its time, providing a solid foundation for modern e-discovery. But as the volume and complexity of data grow, the need for tools that are faster, smarter, and more versatile has become clear.

Generative AI doesn’t just meet these needs—it exceeds them. Its ability to handle multimodal data, process multiple languages, and deliver unmatched accuracy makes it a critical tool for the future of e-discovery. Legal teams adopting GenAI are positioning themselves not just for efficiency, but for success in an increasingly complex legal landscape.

The story of TAR and GenAI is one of evolution—of building on past successes to create something even better. And as more legal professionals embrace the power of generative AI, it’s clear that the future of document review is here, and it’s brighter than ever.