Production Set 13 // Litigation Support Guide

eDiscovery for litigation support teams, 2026 toolkit and skillset.

VERIFIED 21 APR 2026 // INDEPENDENT REFERENCE // NOT LEGAL ADVICE

Litigation support in 2026 is a more technically demanding role than it was five years ago. The processing-to-review handoff is no longer primarily a data wrangling problem, it is increasingly an AI workflow validation problem. Platform coordination, prompt engineering, and statistical sampling are now core skills. This page addresses the full toolkit.

The core litigation support scope is still: collection coordination, processing, review project management, quality control, and production. What has changed is the AI layer between processing and review. The lit-support project manager is now responsible for configuring the AI review tool (writing the issue prompt or designing the seed set), monitoring AI output quality during the review cycle, designing and running the statistical validation sampling, and reporting validation results to the supervising attorney.

Typical lit-support teams in AmLaw and enterprise are 2 to 10 specialists, with a senior project manager (often with a Relativity Certified Administrator or CEDS certification), processing specialists, and review coordinators. The team structure is largely unchanged; the technical requirements within each role are evolving.

Last verified Apr 2026

• •Prompt engineering for document review. Writing effective issue descriptions for LLM relevance scoring is a skill. A vague prompt produces a vague relevance score. Effective prompts specify the issue precisely, reference the relevant legal standard, name known custodians and time periods, and include both inclusive and exclusive criteria. The lit-support specialist is now responsible for drafting prompt candidates and running prompt validation tests against a sample before deploying at scale.
• •Output validation and statistical sampling. Elusion testing, precision measurement, and F1 reporting are now expected lit-support deliverables. The supervising attorney signs off on validation results before production; the lit-support team designs and runs the validation. Grossman-Cormack methodology (95 percent confidence, plus or minus 5 percent margin, random sample of low-scored documents) is the standard reference. See /predictive-coding-2-0 for the full validation framework.
• •Reasoning trace review. Where the platform provides per-document AI reasoning traces (Lighthouse, Nuix Neo, limited Relativity partner API), the lit-support team reviews a sample of traces to verify that the AI's reasoning is consistent with the issue definition and the attorney's coding decisions. A new QC step with no direct predecessor in the pre-GenAI workflow.
• •Privilege module configuration. Configuring the privilege detection module requires inputting the attorney list, legal hold period, joint-defence parties, and known privilege exceptions (e.g., crime-fraud matters). The lit-support specialist is responsible for keeping the attorney list current and running privilege validation sampling before production.
• •Agile TAR project management. The EDRM 2.0 framework includes agile review cycles that overlap with AI training iterations. Lit-support project managers track review velocity, model convergence, and remaining population estimates in real time rather than running a linear review-to-completion workflow. Tools like Relativity's Active Learning Project (ALP) dashboard and Everlaw's review analytics support this.

Last verified Apr 2026