Tracking changes in text documents is easy. Understanding what those changes mean is hard. A redline showing 47 modifications across a 30-page contract does not tell the reviewer which changes are substantive and which are cosmetic. Every change needs to be read and assessed individually.

The Problem

Document version comparison tools show what changed but not why it matters. A reviewer looking at a redlined contract must evaluate each modification for legal, financial, or operational significance. For long documents with many revisions, this is time-consuming and error-prone.

The AI Approach

An LLM can compare two document versions, identify all changes, and produce a plain-language summary categorized by significance. Substantive changes (modified payment terms, altered liability clauses) are highlighted separately from cosmetic changes (reformatting, grammar fixes, section reordering).

Three-Step Build

Step 1 - Version extraction. Extract text from both document versions. If available, use the native track-changes data. Otherwise, run a text-level diff to identify changed sections.

Step 2 - Change classification. Send each change to an LLM with context about the document type. The model classifies each change as substantive (alters meaning or obligation), clarifying (improves language without changing meaning), or cosmetic (formatting, typos).

Step 3 - Summary report. Generate a change summary organized by significance level. For substantive changes, include the old text, new text, and an explanation of how the meaning changed.

Where It Breaks

Subtle language changes that materially alter meaning may be classified as clarifying. Section reordering can confuse diff algorithms, producing false changes. Documents with complex formatting (tables, headers, footnotes) may lose structure during text extraction.

The Production Path

Focus on your highest-value document type first (contracts, regulatory filings, policy documents). Validate classification accuracy with domain experts for the first 50 comparisons. Add approval workflow integration so reviewers can accept or flag each substantive change directly from the summary.