Generative AI for First-Pass Document Review

Large document collections can make first-pass review slow and expensive. Technology-assisted review, often called TAR, uses a review protocol and machine-learning signals to help prioritize documents for human examination. Generative AI can also summarize, classify, or extract clauses, but its fluent output may omit qualifications or fabricate a statement not present in the source. These workflows answer different questions: a relevance classifier may prioritize likely responsive documents, while a summarizer creates a condensed account of selected content. Legal teams should define the review objective, population, privilege handling, and quality checks before processing documents. A sample of the output should be compared with source materials, and teams should examine both missed relevant records and false positives. Performance needs to be measured in the context of the actual corpus and review protocol; a single accuracy figure does not reveal what was missed. Confidentiality, access controls, retention, and vendor terms matter because documents may contain client or personal information. Reviewers should preserve source links, document identifiers, and version history so conclusions can be traced. Any privilege or production decision requires appropriate legal review and compliance with governing rules and orders. First-pass AI may improve navigation, but it cannot decide legal relevance in every context or relieve lawyers of professional responsibilities. Teams should document human oversight and exceptions, especially when a workflow affects deadlines or production scope.