eDiscovery Document Review Best Practices for Speed and Accuracy

Document review is still where most eDiscovery money goes. ComplexDiscovery's 2025 eDiscovery Review Update estimates global spending on review software and services at $10.81 billion in 2024, rising to about $13.05 billion by 2029. Interest in faster methods is high: document review ranked as the leading generative AI use case among legal professionals at 77%, according to LawSites' coverage of the Thomson Reuters Institute 2025 Generative AI in Professional Services Report.

Practice has not caught up with interest. In the Winter 2025 eDiscovery Pricing Survey cited in the same ComplexDiscovery report, nearly one-third of respondents said they had no familiarity with or use of generative AI-assisted review. Many teams still treat speed and accuracy as a trade-off, although both depend largely on how the review workflow is designed.

Speed and Accuracy Fail for the Same Reasons

When a review runs late, the usual response is to add reviewers. When quality slips, the usual response is to add another layer of checking. Both responses raise cost without fixing the cause. In most matters, slow review and inaccurate review come from the same few problems:

• Too much data reaches review. Duplicates, system files, and clearly irrelevant date ranges add hours without adding information.

• The protocol is vague. Reviewers who are unsure how to code an issue slow down, guess, or code the same document differently from their colleagues.

• Documents arrive in random order. Reviewers see the important material late, so issues surface after the team has already set its strategy.

• Quality checks happen at the end. Errors found after thousands of documents are coded are expensive to correct.

A sound document review workflow addresses each of these upstream, before they turn into rework.

Five Document Review Best Practices Improve Speed and Accuracy Together

1. Reduce the Data Set Before Review Begins

Deduplication, email threading, date and custodian filters, and removal of system files should run before any reviewer opens a document. Early case assessment helps teams see what the collection contains and set scope with confidence. Every document removed at this stage is one that never needs to be reviewed, logged, or checked.

2. Write a Protocol Reviewers Can Apply Consistently

A strong protocol defines each issue code with examples, explains how to treat document families and partially privileged material, and names who answers questions. It should be a living document. When reviewers raise the same question twice, the answer belongs in the protocol.

3. Prioritize the Documents Most Likely to Matter

Technology-assisted review and AI-powered document review rank documents by likely relevance as reviewers code them. Key documents reach the team early, and lower-ranked material can be handled through sampling or a lighter review pass. Prioritization also gives case teams earlier insight into the facts that shape strategy.

4. Build Quality Control Into the Workflow

Quality control works best as a continuous process rather than a final step:

• Sample coded documents daily, especially in the first days of review

• Track overturn rates by reviewer and by issue to find where guidance is unclear

• Compare coding across near-duplicates and families to catch inconsistencies

• Route privilege calls with low confidence to a senior reviewer

5. Measure the Review and Adjust Mid-Stream

Review speed, overturn rates, and the share of relevant documents found should be visible throughout the matter. Metrics show when a protocol needs clarification, when a reviewer needs coaching, and when prioritization has surfaced most of the relevant material.

The Right Document Review Platform Makes These Practices Routine

Best practices are easier to sustain when the technology supports them by default. When legal departments compare legal document review platforms, these capabilities make the largest difference:

• Processing and culling in the same environment as review, so data reduction does not require exports between tools

• Built-in prioritization that updates as reviewers code

• Quality control tools for sampling, overturn tracking, and consistency checks across families and near-duplicates

• Real-time reporting on progress, coding rates, and reviewer performance

• A complete audit trail of coding decisions to support defensibility

Point solutions can handle individual steps, but each handoff between tools adds time and creates a gap in the record. This overview of the key benefits of an end-to-end eDiscovery platform for complex cases explains why connected workflows hold up better as matters grow. For teams assessing eDiscovery review platforms, Reveal's approach to eDiscovery brings processing, AI-assisted review, and production into one platform. Teams can schedule a demo to see these practices applied to their own data.

Fast, Accurate Review Starts Before the First Document Is Coded

The choices that determine review speed and accuracy are mostly made early: how much data reaches reviewers, how clearly the protocol defines each call, which documents they see first, and how quickly errors are caught. Teams that settle these questions at the start of a matter spend less time on rework and have a stronger record when their review process is questioned.

To build a document review workflow that delivers both speed and defensible accuracy on your next matter, contact the Reveal team.