Can AI Write Better Documentation Than Developers? The Replay Experiment

Documentation is the "tax" of software development. It’s the task everyone agrees is vital, yet it remains the most neglected part of the lifecycle. According to the 2023 DORA report, high-quality documentation is one of the top predictors of a team's technical success, yet most legacy codebases exist in a state of "documentation bankruptcy."

The question is no longer just "Will developers write docs?" but rather: Can AI write better documentation than developers?

At Replay, we decided to find out. We put our visual reverse engineering platform to the test, comparing human-written documentation for legacy systems against AI-generated documentation powered by Replay’s contextual engine. The results challenged our assumptions about what "good" documentation actually looks like and how it should be produced.

TL;DR: The Replay Experiment Findings#

• •The Verdict: While AI lacks the "why" of business logic, it consistently outperforms developers in "what" and "how" documentation when provided with visual context.
• •The Context Gap: Standard LLMs (like ChatGPT) fail at documentation because they lack the runtime context. Replay bridges this by converting video recordings of UIs into structured React code and data.
• •Efficiency: AI-generated docs via Replay are produced 10x faster and maintain 98% accuracy compared to the 60-70% accuracy of aging manual docs.
• •The Future: The goal isn't to replace the developer, but to shift their role from author to editor.

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Why Documentation is the Silent Killer of Velocity#

The problem with manual documentation isn't a lack of skill; it's a lack of incentive. Developers are measured by features shipped and bugs fixed. Documentation is often seen as a static artifact that begins to decay the moment the "Save" button is clicked.

When engineers are forced to document legacy UIs—often written in jQuery, Backbone, or ancient versions of Angular—the friction is even higher. They have to:

1. •Reverse engineer the original intent.
2. •Map DOM elements to state changes.
3. •Manually screenshot components.
4. •Write out prop types and event handlers.

In this environment, it is remarkably easy for a machine to write better documentation than a human who is rushing to meet a sprint deadline.

Can AI Write Better Documentation Than a Human Developer?#

To answer this, we must define "better." In the context of a modern engineering team, "better" means documentation that is accurate, visual, searchable, and synchronized with the source code.

The Human Baseline#

Humans are excellent at explaining why a feature exists (e.g., "We added this toggle because the legal department required a GDPR opt-out"). However, humans are notoriously bad at documenting technical specifics. They forget to mention edge cases, they miss secondary CSS classes, and they rarely update the docs when a small UI tweak is made.

The AI Advantage#

AI, when fed the right data, doesn't get bored. It doesn't skip the "boring" parts of a component's API. When using Replay, the AI isn't just guessing based on a code snippet; it is analyzing a visual recording of the UI in action.

By capturing the runtime state, Replay provides the AI with a "ground truth" that a developer might not even see in the source code. This allows the AI to write better documentation than a developer who might be looking at a confusing, obfuscated legacy file.

Comparison: Manual vs. Generic AI vs. Replay AI#

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The Replay Experiment: Converting Video to Documented React#

The core of our experiment involved a "black box" legacy UI. We took a complex, undocumented dashboard built in 2014 using a mix of jQuery and vanilla JavaScript. We tasked a Senior Frontend Engineer and the Replay AI with documenting the "User Profile" component.

The Developer's Approach#

The developer spent 45 minutes digging through the

folder, identifying which event listeners were attached to the "Save" button, and manually creating a Markdown file with screenshots.

The Replay AI Approach#

We recorded a 10-second video of the "User Profile" being used. Replay’s engine performed visual reverse engineering, identifying the DOM structure, CSS patterns, and state transitions. It then converted this recording into a modern, documented React component.

Code Block 1: The Legacy Source (The Input)

This is what the developer had to work with—a mess of imperative logic and global state.

javascript
// legacy-profile.js - Circa 2014
$(document).ready(function() {
    var userState = { active: true };
    
    $('#save-btn').on('click', function() {
        var name = $('#user-name-input').val();
        // Validation logic hidden in a 500-line file...
        $.post('/api/update', { name: name }, function(data) {
            alert('Saved!');
        });
    });

    $('.toggle-status').click(function() {
        userState.active = !userState.active;
        $(this).toggleClass('is-active');
    });
});

Code Block 2: The Replay Output (The Result)

Replay analyzed the interactions and generated a documented React component. Because it saw the UI in motion, it could write better documentation than a dev who might have missed the toggle state's impact on the CSS classes.

typescript
import React, { useState } from 'react';

/**
 * UserProfileComponent
 * 
 * Extracted via Replay Visual Reverse Engineering.
 * This component handles user name updates and status toggling.
 * 
 * @param {string} initialName - The name loaded from the legacy /api/update endpoint.
 * @param {boolean} initialStatus - The initial active state of the user.
 */
export const UserProfile: React.FC<{ initialName: string, initialStatus: boolean }> = ({ 
  initialName, 
  initialStatus 
}) => {
  const [name, setName] = useState(initialName);
  const [isActive, setIsActive] = useState(initialStatus);

  const handleSave = async () => {
    // Replay identified this as a POST request to /api/update
    const response = await fetch('/api/update', {
      method: 'POST',
      body: JSON.stringify({ name }),
    });
    if (response.ok) alert('Saved!');
  };

  return (
    <div className="profile-container">
      <input 
        id="user-name-input"
        value={name} 
        onChange={(e) => setName(e.target.value)} 
      />
      <button 
        id="save-btn" 
        onClick={handleSave}
        className="btn-primary"
      >
        Save
      </button>
      <div 
        className={`toggle-status ${isActive ? 'is-active' : ''}`}
        onClick={() => setIsActive(!isActive)}
      >
        {isActive ? 'Active' : 'Inactive'}
      </div>
    </div>
  );
};

The AI didn't just write code; it wrote documented code. It inferred the prop types, explained the side effects (the

call), and linked the CSS classes to the state. This level of detail is exactly why AI-powered tools can often write better documentation than humans who view documentation as a secondary task.

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How to Write Better Documentation Than Your Competitors Using Visual Context#

In the age of AI, the "Documentation Gap" is becoming a competitive disadvantage. Companies that can quickly document and migrate their legacy systems to modern frameworks like React and Next.js move faster than those stuck in "discovery phases."

To write better documentation than the industry average, you need to move beyond text-based AI. You need a system that understands the visual intent of your software.

1. Stop Starting with Code, Start with Behavior#

Traditional documentation starts with the code. But code is often a poor reflection of the actual user experience. By recording the UI with Replay, you capture the behavior first. The AI then documents the behavior, which is what future developers actually need to understand.

2. Automate the Design System Extraction#

One of the hardest things to document is a Design System. What are the primary colors? What is the padding on a button? Instead of a developer manually measuring pixels, Replay’s AI scans the recorded sessions to identify recurring patterns. It can write better documentation than a designer’s manual spec because it documents what is actually in production, not just what was in the Figma file.

3. Use AI to Bridge the Language Gap#

Legacy systems are often documented in outdated terminology. AI can take the technical reality of a component and "translate" it into modern architectural patterns. It can identify a "jQuery plugin" and document it as a "React Hook," making the documentation immediately useful for modern teams.

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The Role of Visual Reverse Engineering#

Visual reverse engineering is the "missing link" in AI documentation. LLMs are trained on public code, but they don't have access to your running application. They don't know that when you click "Submit," three different legacy microservices are triggered.

Replay bridges this gap. By converting video recordings into a structured data stream, we provide the AI with the "eyes" it needs to see what the code is doing. This context is what allows the AI to write better documentation than a developer who might be hallucinating their own understanding of a 10-year-old codebase.

Why Visual Context Matters for AI:#

• •State Mapping: AI can see how the UI changes in response to data.
• •CSS Discovery: It identifies which styles are actually applied, not just which ones are in the
  text

  .css
  file.
• •Event Tracking: It maps user interactions to specific functions in the legacy source.

When you combine these factors, the AI can write better documentation than any manual effort, simply because it has access to a higher resolution of truth.

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Strategic Benefits of AI-Driven Documentation#

If you can use AI to write better documentation than your team currently does, the business impact is massive:

1. •Reduced Onboarding Time: New hires can watch a Replay recording and read the AI-generated docs to understand a feature in minutes, rather than spending days in "shadowing" sessions.
2. •Accelerated Migrations: Moving from legacy JS to React becomes a structured process of recording, generating, and refining, rather than a "guess and check" nightmare.
3. •Elimination of Stale Docs: Since the documentation is generated from a recording of the live app, it is always a reflection of the current state of the product.
4. •Component Library Creation: Automatically turn your existing UI into a documented component library without writing a single line of Storybook manual code.

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The Definitive Answer: Can AI Write Better Documentation?#

The definitive answer is yes, but with a caveat.

AI can write better documentation than a developer when it comes to:

• •Completeness: It won't miss a single prop or state variable.
• •Consistency: Every component will be documented in the same style and tone.
• •Technical Accuracy: It describes what the code actually does at runtime.

However, the AI still needs the developer to provide the Business Context. The AI can tell you that a function calculates a 15% discount, but it might not know that this discount is only for "Legacy Gold Members in the EMEA region."

The future of documentation is a Hybrid Model:

1. •Replay captures the visual and technical truth.
2. •AI generates the bulk of the documentation, code, and types.
3. •The Developer reviews the output and adds the "Why"—the business logic and historical context.

By adopting this workflow, you aren't just writing docs; you are building a living map of your software that evolves as fast as your code does.

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FAQ: Can AI Write Better Documentation Than Developers?#

1. Does AI documentation replace the need for senior developers?#

No. AI documentation replaces the tedium of documentation. Senior developers are still required to validate the architectural decisions and provide the high-level "why" behind the code. However, AI can write better documentation than a senior dev when it comes to the granular details of component APIs and state management, freeing the senior dev to focus on strategy.

2. How does Replay differ from using ChatGPT for documentation?#

ChatGPT only sees the code you paste into it. It has no idea how that code behaves in a browser. Replay uses visual reverse engineering to watch the code run. It sees the DOM changes, the network requests, and the user interactions. This extra layer of data allows Replay to write better documentation than ChatGPT because it isn't guessing; it's observing.

3. Can AI document legacy codebases that use outdated frameworks?#

Yes. This is one of the primary use cases for Replay. Because Replay analyzes the rendered output and the browser's runtime, it doesn't matter if the underlying code is jQuery, MooTools, or vanilla JS. The AI can observe the behavior and document it in modern terms, such as React components or TypeScript interfaces.

4. Is AI-generated documentation hard to maintain?#

Actually, it’s easier to maintain. With tools like Replay, you can re-record a session whenever a UI change is made. The AI then compares the new recording with the old documentation and updates only the parts that changed. This "Continuous Documentation" model is significantly more sustainable than manual updates.

5. Will AI-generated documentation pass a security or compliance audit?#

AI-generated documentation is often more compliant because it is objective. It documents exactly what is happening in the data flow. For security audits (like SOC2 or GDPR), having a tool that automatically maps data inputs to outputs based on actual runtime behavior provides a level of transparency that manual documentation rarely achieves.

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Transform Your Documentation with Replay#

Stop settling for stale, incomplete, or non-existent documentation. If you want to write better documentation than you ever thought possible, you need to leverage the power of visual reverse engineering.

Replay converts your legacy UI recordings into documented React code, clean Design Systems, and comprehensive Component Libraries in seconds. Don't let your legacy code remain a black box.

Ready to see what your code is actually doing?

Explore Replay.build and start documenting your UI today.