Generative AI's Impact on Ediscovery
Generative AI – particularly large language models (LLMs) like ChatGPT – is rapidly transforming ediscovery. Legal teams are beginning to leverage these AI systems to automate tedious discovery tasks, gain faster insights, and improve accuracy in document review. Generative AI represents a major shift from traditional ediscovery methods—such as keyword searching and early predictive coding—by grasping context and producing nuanced, human-like document summaries and analyses.
This chapter discusses how generative AI is impacting ediscovery in multiple ways, including: automation of review and analysis tasks, comparison to traditional TAR, accuracy of AI-driven coding suggestions, responsible use principles, real-world use cases and best practices for implementation.
Ediscovery Use Cases for Generative AI
Generative AI is being applied to automate many time-consuming tasks in the ediscovery process, including significantly accelerating document review, quickly summarizing documents, streamlining privilege review and logging, identification of named entities like key people and organizations, extraction of important topics, and automating writing tasks (such as drafting deposition questions, creating narrative timelines from evidence, and drafting legal briefs).
Document Review Acceleration
One major use is document review acceleration. Modern LLM-driven tools can analyze vast collections of documents and suggest how they should be coded (e.g. responsive vs. non-responsive or privileged vs. not privileged) in a fraction of the time a human review would take. Generative AI solutions like EverlawAI Assistant can apply Coding Suggestions across thousands of documents at once, using prompts that describe the case and coding criteria.
In a test on real litigation data, Coding Suggestions classified documents as responsive or not with accuracy comparable to human reviewers – even outperforming humans in recall by 36% on one dataset. Such results indicate that first-pass document review, typically a labor-intensive phase, can be largely automated with GenAI, reducing the burden on legal teams.
Document Summarization
Another key task being transformed is summarization. GenAI can rapidly digest lengthy or complex documents and produce coherent summaries or highlights. EverlawAI Assistant, for instance, can generate a summary for a single document or even batch summarize up to 1,000 documents at one time. This capability is invaluable for quickly understanding evidence – e.g. summarizing email threads, reports, or deposition transcripts – and for tasks like creating chronology timelines or investigative outlines.
In one case, a legal analyst used EverlawAI Assistant to summarize more than 150 court cases (over a year’s worth of case law) into a concise study guide, identifying the most relevant cases for the attorneys. In another example, a firm faced with hundreds of discovery requests (spanning 120+ pages) had the AI produce a condensed 2-page summary of the requests within minutes, instead of assigning a junior attorney days of work. These examples illustrate how GenAI-driven summarization streamlines review and analysis, giving lawyers faster insight into the substance of their data.
Privilege Review and Logging
Generative AI is becoming increasingly valuable in privilege review and log creation, traditionally among the most time-consuming aspects of ediscovery. Legal-specific language models can analyze document content and automatically draft standardized privilege log entries, including justifications for withholding documents. In more advanced implementations, custom-tuned LLMs generate tailored privilege summaries, capturing both content and the legal basis for privilege. This automation streamlines what has historically been a manual and labor-intensive process, delivering estimated time savings of up to 40% in privilege log preparation and review.
The technology can swiftly identify likely privileged communications – such as attorney–client exchanges – and categorize them by topic, enhancing both consistency and accuracy. With the ability to detect subtle contextual cues, generative AI may even surface privileged material that human reviewers might miss. While human attorneys still oversee and validate these outputs, offloading the initial drafting to AI can substantially reduce both costs and turnaround times.
Case Strategy and Early Case Assessment
GenAI is increasingly being leveraged in litigation to develop case strategy by synthesizing large volumes of legal and factual data into actionable insights. By analyzing pleadings, motions, deposition transcripts, and discovery documents, generative AI tools can identify patterns, legal arguments, and potential weaknesses in a case. AI-driven narrative generation can also assist in creating preliminary case theories, timelines, and trial storylines, enhancing the team’s ability to craft persuasive arguments that resonate with judges and juries. Importantly, AI-generated outputs can be used iteratively, allowing lawyers to test and refine multiple strategic paths before committing to a litigation course.
In ECA, generative AI accelerates the ability to evaluate a case's merits, risks, and potential value by rapidly reviewing and summarizing key documents, communications, and metadata from custodians and systems. For example, AI language models can perform named entity recognition and topic extraction, pulling out key people, organizations, places, or themes across a dataset to reveal hidden connections. This early insight helps in estimating likely costs, timelines, and exposure, enabling more informed decisions about settlement, further discovery, or trial preparation.
Timelines of Key Events
GenAI can play a valuable role in developing litigation timelines by extracting, organizing, and summarizing key events from large volumes of unstructured data such as emails, memos, contracts, deposition transcripts, and chat messages. By identifying relevant dates, actors, and contextual relationships, generative AI can automatically generate chronological narratives that highlight significant developments, actions taken, and turning points in the case.
These AI-generated timelines help legal teams quickly understand the sequence of events, uncover potential gaps or inconsistencies in the record, and visually present the case story to support internal strategy discussions, expert analysis, or courtroom presentations.
Identification of Image Contents
GenAI can assist in discovery by identifying and analyzing the contents of images, enabling legal teams to extract meaningful information from visual evidence that would otherwise require manual review. Using computer vision and natural language processing, generative AI can detect objects, faces, locations, timestamps, text within images (via optical character recognition), and even contextual clues such as emotional expressions or brand logos.
This capability allows for automated classification, tagging, and issue coding of images, helping teams quickly locate relevant visuals tied to key claims or defenses – such as identifying unsafe conditions in product liability cases or verifying document authenticity in fraud investigations.
Translation of Foreign Language Documents
GenAI can streamline the translation of documents in discovery by quickly and accurately converting text from one language to another, enabling legal teams to access and review foreign-language evidence without delay. Unlike traditional machine translation tools, generative AI models can understand legal context, idiomatic expressions, and nuanced phrasing, producing more fluent and contextually appropriate translations.
This is especially valuable in cross-border litigation, international arbitration, or matters involving multilingual custodians, where time-sensitive review of contracts, communications, or regulatory materials is critical. Additionally, AI-driven translation can be integrated with document review workflows, allowing for real-time translation, issue tagging, and privilege assessment – all while maintaining traceability to the original language for validation and compliance purposes.
Adoption of Generative AI for Ediscovery Use Cases
All these applications underscore the fact that generative AI acts as a force-multiplier in ediscovery, automating low-level work (classification, summarizing, information extraction) so that attorneys can focus on strategy and substantive analysis.
Research shows that the above use cases for generative AI are being applied in discovery by a growing number of legal and ediscovery professionals. According to the 2025 State of the Industry report by eDiscovery Today, more than 30% of 551 discovery professionals surveyed said they apply LLMs and generative AI to seven different ediscovery use cases, with two of those use cases – Document Review Automation (at 62.3%) and Document Summarization (at 57.9%) – being applied by more than half of respondents.
Applying generative AI to a variety of use cases is no longer theoretical – organizations are already applying it in actual practice today. Potential use cases for generative AI will continue to grow as organizations transform their processes and workflows to fully leverage its capabilities.
Generative AI vs. TAR/Predictive Coding
As discussed in our chapter on predictive coding, the discipline of predictive coding, which is often referred to as TAR, has been a staple in ediscovery for more than a decade.
Typically, a reviewer begins by tagging a seed set of documents, which the system uses to learn and iteratively refine its model through continuous active learning. This method has a well-established track record and is widely accepted by courts, with numerous legal precedents affirming the defensibility of TAR in document review. When paired with proper validation techniques, such as statistical sampling, TAR delivers reliable performance at scale, offering consistent prioritization of relevant materials and achieving high levels of precision and recall.
Generative AI–powered review, by contrast, takes a different approach. Instead of learning solely from case-specific human tagging, an LLM-based system leverages a pre-trained foundation model that has learned language patterns from billions of words of text.
In an ediscovery context, a generative AI tool uses the LLM’s language understanding to classify documents based on natural language prompts rather than purely on examples. A user provides instructions – e.g. descriptions of what constitutes relevance or privilege in the case – and the AI applies those criteria to the document set. This means GenAI can be deployed immediately on a dataset with minimal training, as long as the reviewer can effectively articulate the review criteria. The process becomes more about writing and refining the right prompts (instructions) for the AI, versus coding thousands of training documents as in TAR. In practice, teams often use an iterative approach: test the AI on a sample, refine the prompt if needed, and then apply it broadly.
Transparency of Generative AI vs. TAR
Transparency is one area of distinction between traditional TAR and newer generative AI. TAR workflows, while employing algorithms, are generally accompanied by statistical validation methods and a relatively straightforward explanation (e.g. a stable algorithm like logistic regression) that can be described in court. Many TAR tools provide metrics like precision, recall, and richness, and some allow insight into how the model was trained (e.g. which documents were used). Aside from that, however, TAR tools generally don’t provide insights in a user-friendly manner as to why individual documents were classified the way they were (you may only get a score for the document, but not information on how that score was determined), so the only way to confirm the documents were classified correctly is to manually review them to confirm the TAR classification is correct based on their content.
General LLMs are often criticized as “black box” systems – they typically produce outputs without a clear explanation as to how that output was determined. However, some generative AI review tools for ediscovery have built-in features to improve transparency.
EverlawAI Assistant, for instance, limits analysis to the four corners of the document (the content provided) and supports each output with citations to the source text or Bates numbers. With a single click, a user can see exactly which passage led the AI to mark a document as responsive or privileged. This approach helps mitigate the black-box issue by giving reviewers and auditors a concrete basis to understand and trust the AI’s decisions.
Accuracy of Generative AI vs. TAR
In terms of accuracy, generative AI is showing very promising results – in some cases rivaling or exceeding the accuracy of traditional TAR or human review. TAR systems have demonstrated high accuracy in many projects, but they require careful tuning and are only as good as the consistency of the human training examples. Generative AI’s strength is in its ability to grasp semantic meaning and context beyond simple keyword overlaps. By comprehending context and substantive content, AI systems appear to offer a more precise approach to identifying relevant documents than traditional keyword-based or predictive coding techniques.
As a result, LLMs can recognize when different words or phrases have the same meaning in context and can pick up on subtle cues (like tone or implication) that rule-based TAR might miss. Early experiments combining generative AI with document coding have reported precision and recall equaling or even exceeding recall for manual review by as much as 36%.
However, it’s important to note that reliability can vary by case. The AI’s performance will depend on the clarity of the prompt, the homogeneity of the data, and the complexity of the criteria. TAR, being a more mature technology, has well-established validation practices – and courts have confidence in it – whereas generative AI is still earning trust through ongoing testing.
Usability of Generative AI vs. TAR
Usability and user experience are arguably where generative AI diverges most from traditional TAR. TAR typically requires significant setup – involving model training on example documents, often through multiple rounds of review and feedback. This can be time-intensive and may require oversight from experienced ediscovery professionals. Its interfaces are generally geared toward specialists, featuring control sets, performance metrics, and detailed dashboards.
In contrast, generative AI platforms frequently offer intuitive, natural language interfaces that resemble smart assistants. Users can ask straightforward questions – such as “Find emails with an angry or urgent tone” – or issue simple instructions like “Summarize this 100-page report” or “Identify documents related to topic X,” and receive immediate, contextually relevant responses. This ease of use significantly lowers the barrier to entry, enabling legal professionals with minimal technical background to interact with the AI effectively, without the need for specialized workflows or consultants.
Also, the fact that generative AI is not limited to classification tasks – and can assist with drafting, answering questions, summarizing content, and more – makes it a versatile tool that supports a wide range of legal functions within a single platform. This adaptability encourages broader adoption across legal teams and accelerates integration into daily workflows.
Of course, this convenience also comes with important caveats. Generative models will attempt to answer any query, even if it requires making assumptions or producing inaccurate results (i.e., hallucinations). This contrasts with TAR, which is more conservative and will simply avoid classifying documents when confidence is low. As a result, guardrails, validation processes, and user training are essential when deploying generative AI in legal contexts.
Bottom Line
For many in the industry, the approach for now is that these tools are complementary. TAR’s established track record and judicial approval continue to make it a defensible and effective review tool, whereas generative AI adds a complementary layer of efficiency and depth, capable of identifying contextual nuances that earlier algorithms might miss.
In fact, some workflows use both in tandem: in one case, a legal team used EverlawAI Assistant to jump-start the review (provide quick coding suggestions), then used a TAR 2.0 process to statistically validate the AI and to prioritize any documents for human second-look. Such hybrid approaches leverage the speed of GenAI and the proven consistency of TAR together.
Ultimately, generative AI should be viewed as an evolution of AI in ediscovery – enhancing, rather than replacing, TAR. While TAR remains a trusted and defensible baseline for large-scale review, generative AI introduces greater flexibility, deeper contextual analysis, and faster insights. Used together, they form a complementary approach that combines the reliability of TAR with the speed and sophistication of generative AI. Looking ahead, it’s logical to expect TAR to continue serving as the foundation for defensible workflows, with generative AI layered on top to elevate the overall efficiency and analytical depth of ediscovery efforts.
Accuracy of AI Coding Suggestions and Underlying Mechanisms
One of the remarkable claims about recent generative AI in ediscovery is the high accuracy of AI-driven coding suggestions. As noted above, controlled evaluations have shown LLM-based classification reaching human-level precision and recall in identifying relevant documents.
For example, in one documented instance with EverlawAI Assistant’s Coding Suggestions, it achieved about 77% precision and 82% recall in tests, which actually surpassed the first-pass human review recall rate by a significant margin. Achieving these precision and recall numbers required advances in the underlying mechanisms of the AI, which differ fundamentally from older rule-based or small-model approaches.
Additionally, in Everlaw’s suggested coding suggestions workflow, the user provides contextual prompts that outline the case and coding definitions (for example, a prompt might include a summary of the case issues, a definition of what is considered responsive, and examples of privileged material). The LLM uses this guidance to evaluate each document’s text and determine how it should be coded, effectively simulating the thought process of a human reviewer but at machine speed. Because the model has learned language and legal patterns from millions of documents, it can pick up subtle indicators – for example, it might recognize that an email starting with “Dear Counsel” and discussing legal advice is likely privileged, even if certain key terms aren’t present.
To further enhance accuracy, these generative models often undergo fine-tuning and alignment procedures such as reinforcement learning from human feedback (RLHF). RLHF is a training technique where human evaluators provide feedback on the AI’s outputs, and the model learns to prefer outputs that humans rate as helpful or correct. This has been key in training AI like ChatGPT to follow instructions and avoid nonsensical answers. In the context of ediscovery, continuous feedback loops can be established: reviewers correct the AI’s suggestions (e.g. mark a suggestion as wrong), and over time the system can adjust to those corrections.
For example, as the AI gets more domain-specific feedback – such as learning from thousands of privilege determinations – it can further improve its judgment in line with expert reviewers. Even without explicit fine-tuning by each user, the general technique of RLHF used during the model’s development means the LLM is better aligned with human-like decision-making out of the box. OpenAI’s GPT-4 (a foundation model behind some legal AI tools) is an example of a model that was trained with human feedback to produce answers that a human would consider valid and well-justified.
It’s also worth noting that document review via generative AI is typically constrained to the provided data and required to cite supporting evidence, which helps maintain accuracy and prevents hallucinations. By tying every suggestion to an actual snippet from a document, the AI’s outputs remain grounded and verifiable in context. Essentially, the AI is performing a kind of augmented reasoning: it uses its language understanding to determine relevance but backs up each determination with real evidence from the dataset (much like a good reviewer would refer to document text to justify their call). This mechanism builds trust in the AI’s accuracy, since reviewers can quickly validate why the AI labeled a document a certain way. If the reasoning looks off, they can adjust the criteria or correct the AI.
While no system is perfect, the early data suggests that with proper setup, a generative model can detect relevant documents with similar or better fidelity than traditional reviews – which is a major milestone in ediscovery technology. As the models continue to improve and accumulate legal-specific training (via RLHF or fine-tuning on legal texts), we can expect accuracy and reliability to further increase.
Principles of Responsible Use of Generative AI in Ediscovery
With great power comes great responsibility – and generative AI is no exception. The legal field demands high standards of ethics, accuracy, and security, so deploying generative AI in ediscovery must be done with careful controls to address potential risks.
Control and Transparency
Users should have the ability to determine whether to engage with generative AI tools, including the ability to opt in or out based on their preferences, risk tolerance, or compliance requirements. When generative AI is activated, all AI-driven interactions should be clearly identified, ensuring users understand when they are receiving content or insights generated by an AI system.
Confidence
Generative AI offers significant value by enhancing efficiency, insight, and automation—but like any advanced technology, it is not immune to error. To foster user trust and responsible adoption, it is essential to design AI tools that promote confidence in their outputs. This includes focusing generative AI capabilities on well-defined, high reliability use cases where performance has been validated. Wherever feasible, AI-generated results should be supported by citations to specific, verifiable passages within the underlying evidence, allowing users to trace the rationale behind the output and independently assess its accuracy.
Privacy and Security
When using any third-party generative AI tool, organizations must carefully assess the system’s operational framework from both a legal and security standpoint. This includes evaluating the types of data the tool will process, the protective measures in place to ensure data confidentiality and integrity, and whether the provider’s practices align with applicable laws, internal policies, and client commitments.
A critical consideration is ensuring that third-party tools offer robust controls to prevent the use of customer data for model training or retention – i.e., zero data retention of customer data. Additionally, organizations should maintain transparency by clearly disclosing which third-party AI services are in use, what roles they play in the workflow, and how data is handled within those systems.
Best Practices for Leveraging Generative AI in Ediscovery
Successfully integrating generative AI into ediscovery requires more than just flipping a switch – it calls for thoughtful strategy and best practices to maximize benefits and mitigate risks. Legal teams and service providers can consider the following best practices when adopting GenAI tools in their ediscovery workflows.
Start with Pilot Projects and Small-Scale Tests
GenAI may be powerful, but it’s still new for many organizations. It’s wise to begin on a small scale, using closed matters or a subset of documents to see how the AI performs before relying on it in an active case.
By running the AI on a known dataset (perhaps one that has already been reviewed by humans), the team can gauge its accuracy, identify any quirks, and get comfortable with its operation without jeopardizing an active matter. Essentially, crawl before you walk: use pilot projects as a training ground for both the AI and your team.
Define Clear Use Cases and Choose the Right Tool for the Job
GenAI is not one-size-fits-all. It excels at some tasks and is less suitable for others. One best practice is to identify where AI will add the most value in your specific workflow and deploy it there, rather than trying to apply it indiscriminately. For example, if you have a case with millions of mostly redundant emails, AI coding suggestions might dramatically cut down the review set. However, if you have a smaller case with highly complex technical documents, traditional review or expert involvement might still be necessary.
It’s also important to recognize scenarios where LLMs thrive, such as linear analyses or concept-based classifications, versus scenarios that remain challenging, such as analyzing non-text data like images or deciphering extremely domain-specific jargon without fine-tuning. Many teams are finding that generative AI can handle the bulk of standard reviews, while unusual documents or anything the AI flags as uncertain can be routed to human experts.
Involve Reviewers in Prompt Engineering
The quality of AI outputs in ediscovery often depends on the quality of the prompts/instructions provided. Legal teams should treat prompt crafting as a new skill in the review process. Reviewers who intimately know the case facts and goals should collaborate in writing and refining these prompts, using a case description, category descriptions, and code criteria as part of the prompt to guide the AI – essentially mirroring what you’d explain to a human review team on day one.
During initial test runs, it’s also important to examine the AI’s mistakes and adjust your instructions accordingly. For example, if the AI is marking all financial emails as relevant just because they mention money, maybe clarify that financial records are only relevant if tied to the disputed contract. Also, maintain a library of successful prompts for different scenarios – over time, you might develop templates for use cases like FCPA investigations vs. employment cases.
Establish Validation and QC Checkpoints
Integrate quality control steps to continually validate the performance of the AI. Some AI tools provide metrics if you input a portion that was manually reviewed for comparison. Over the course of a project, maintain a log of AI accuracy – if you notice precision dropping or a particular type of error recurring, recalibrate or retrain the AI on those.
It’s also recommended to do case-by-case validation; don’t assume that because the AI performed well on Case A that it will be equally great on Case B with very different data. Start each new matter with a validation test. Additionally, implement continuous QC: as human reviewers proceed (e.g. doing privilege review), if they find a document that was misclassified by AI, funnel that information back (some platforms allow flagging it so the AI could learn, or at least the team learns to adjust the prompt). This mindset of treating the AI like a “junior reviewer” whose work needs to be spot-checked regularly helps ensure errors are caught early and provides evidence you can present if needed to demonstrate a defensible process.
Educate and Train Your Team
Generative AI in ediscovery may require a mindset shift and new skills for your legal team. Conduct training sessions for attorneys and paralegals on how the AI works, its limitations, and how to interpret its output. Encouraging a level of AI literacy will make users more effective – for example, teaching them how to phrase follow-up queries if the first answer isn’t useful, or how to correct an AI summary by providing feedback prompts. It’s also important to set expectations: clarify that the tool is there to assist, not to do the thinking for them. Highlight past successes (and failures) from pilot programs to illustrate appropriate use.
For litigation readiness, train the team on how to defend the use of AI if challenged – this includes being able to explain in depositions or court declarations how the AI was used responsibly, how it was validated, and perhaps even how to translate an AI’s decision process into human terms (with those citations to documents, etc.).
Some best practices documentation suggests preparing an “AI protocol” for your matters – similar to an ESI protocol – which outlines how AI will be used and validated; having everyone on the team aware of that protocol is key. The more your reviewers understand the tool, the better they can leverage it and guard against its pitfalls.
Collaborate and Seek Expert Help
If your team is new to AI, partner with those who have experience – whether it’s the software provider, an ediscovery consultant, or even cross-functional collaboration with your IT/data science department. Some providers offer customer success teams or specialists in AI who can assist in setting up workflows and interpreting results. Making use of that expertise can flatten the learning curve.
Likewise, share knowledge within the community – many legal conferences and forums (e.g. Sedona Conference, EDRM) now have working groups on GenAI. Learning from peers about what did or didn’t work can save you from reinventing the wheel. Essentially, treat this as an ongoing learning process: the tech will continue to evolve, and so will best practices. The legal teams that remain informed of advancements and continuously refine their processes will benefit the most. Being open to innovation but doing so with trusted partners and a testing mindset is a formula for success.