Release Notes

Harvey Academy Certification: AI Analysis for Legal Workflows

Learn how to run large-scale AI document analysis and get consistent, verifiable results.

Release Date
Jun 17, 2026
Categories
User Experience
Release Type
Regional Availability
US, EU, AU

What’s New

Harvey Academy has launched a new certification: AI Analysis for Legal Workflows. This certification teaches legal professionals how to run large-scale AI document analysis confidently, from setting up tasks correctly and designing reliable extraction prompts, to verifying output before it reaches a client or filing.

image of a certificate for completing the AI Analysis for Legal Workflows course

Why It Matters

Large-scale document analysis is one of the most time-intensive tasks in legal work. As legal teams increasingly turn to AI to handle it, the margin for error doesn't shrink. Unstructured tasks, poorly designed prompts, and unverified outputs can create more work than they save, and carry real professional risk.

The AI Analysis for Legal Workflows certification gives legal professionals a repeatable framework for getting it right: directing AI precisely, understanding how it behaves at scale, and catching errors before they matter.

How to Use

Access the certification here.

You may be prompted to sign in or register a Skilljar account to enroll in the course. If you have not signed in to Skilljar previously, you’ll need to register to proceed.

FAQs

Q: Who can access this course?

The AI Analysis for Legal Workflows certification is open to the public, anyone can enroll in for free.


Q: Who should take this course?

This course is for legal professionals who regularly extract consistent data points across large document sets and need to verify that output. Some familiarity with generative AI chat tools is assumed.

Q: Is the course Harvey-specific?

Not entirely. Three of the four modules focus on a tool-agnostic approach to analyzing large document sets with AI — applicable regardless of platform. The fourth covers Harvey's approach to large-scale review using Review Tables.