How to Use Video Recordings to Train UI-Aware LLMs with Replay

Software teams are currently drowning in $3.6 trillion of global technical debt. Most of this debt isn't just old logic; it's trapped in brittle, undocumented user interfaces that no living developer fully understands. When teams try to modernize these systems using standard Large Language Models (LLMs), they hit a wall. LLMs are historically "blind" to temporal UI behavior—they see a screenshot, but they don't see the state changes, the hover effects, or the complex multi-page navigation.

Replay changes this by providing the high-fidelity data needed to bridge the gap between visual intent and production code. By using video as the primary data source, we can finally give AI agents the context they need to build real software.

TL;DR: Standard LLMs fail at UI tasks because they lacks temporal context. Replay uses video recordings to train UI-aware models, capturing 10x more context than screenshots. This process, known as Visual Reverse Engineering, allows Replay's Headless API to feed AI agents (like Devin) the exact data needed to generate pixel-perfect React components, design systems, and E2E tests in minutes rather than weeks.

What is a UI-Aware LLM?#

A UI-Aware LLM is a model trained to understand not just static images, but the functional relationships, transitions, and state logic of a user interface. Standard models like GPT-4o are excellent at describing what is in a picture, but they struggle to explain how a "Submit" button changes the DOM across a five-second interaction.

Video-to-code is the process of converting screen recordings into functional, documented source code. Replay pioneered this approach by treating video as a rich temporal dataset rather than a sequence of isolated frames.

According to Replay's analysis, static screenshots miss 90% of the logic required to rebuild a component. You can't see a "loading" state in a static PNG. You can't see how a dropdown menu handles a collision with the bottom of the viewport. Video recordings train UI-aware systems to recognize these patterns automatically.

Why video recordings train uiaware models better than static screenshots?#

If you give an AI a screenshot of a dashboard, it guesses the CSS. If you give an AI a video of that dashboard being used, it observes the CSS in action. This distinction is why 70% of legacy rewrites fail when using traditional manual methods. Developers spend 40 hours per screen trying to mimic behavior that Replay extracts in 4 hours.

Industry experts recommend moving away from "snapshot-based" training. Video provides the "why" behind the "what." When video recordings train UI-aware agents, the resulting code includes:

• •Temporal State Changes: How the UI reacts to user input over time.
• •Navigation Logic: Multi-page flows detected through temporal context.
• •Z-Index and Layering: Understanding which elements sit on top of others during animations.

The Replay Method: Record → Extract → Modernize#

We use a specific methodology to ensure that video recordings train UI-aware models with surgical precision. This isn't just "recording a screen"; it's a structured extraction process.

1. Behavioral Extraction#

First, you record the UI in its natural environment. Replay’s engine doesn't just look at pixels; it analyzes the visual changes to map out the "Flow Map." This allows the AI to see that clicking "User Settings" leads to a specific modal, which then triggers an API call.

2. Component Deconstruction#

Replay identifies recurring patterns. If a button appears on twelve different pages, Replay recognizes it as a single source of truth. It extracts the brand tokens—colors, spacing, typography—and builds a centralized Design System.

3. Agentic Code Generation#

This is where the Headless API comes in. AI agents like Devin or OpenHands use Replay's API to pull structured data. Instead of the agent "guessing" the layout, Replay hands it a JSON schema of the entire UI.

typescript
// Example: Using Replay Headless API to extract component data
import { ReplayClient } from '@replay-build/sdk';

const client = new ReplayClient({ apiKey: process.env.REPLAY_API_KEY });

async function extractUIContext(videoUrl: string) {
  // The Replay engine processes the video to train the UI-aware model context
  const session = await client.processVideo(videoUrl);
  
  const components = await session.getComponents();
  const designTokens = await session.getDesignTokens();

  return {
    components,
    designTokens,
    flowMap: await session.getFlowMap()
  };
}

How video recordings train uiaware systems for legacy modernization#

The world runs on "zombie software"—apps built in COBOL, Flex, or Silverlight that are too risky to touch but too old to maintain. Manual migration is a nightmare. Developers have to read the old code, understand the business logic, and then rewrite it in React.

Replay bypasses the need to read the old, messy source code. By recording the "black box" of the legacy UI, you provide the perfect training set. The video recordings train UI-aware models to understand the intended outcome. The AI doesn't care if the backend is a 30-year-old mainframe; it sees the UI behavior and generates a modern, type-safe React equivalent.

Legacy UI Modernization is no longer a multi-year risk. It becomes a streamlined pipeline.

Generating Production-Ready React#

When Replay generates code, it isn't "spaghetti code." It produces modular, reusable components that follow modern best practices. Here is an example of a component extracted from a legacy screen recording using Replay's Agentic Editor.

tsx
import React from 'react';
import { useTheme } from './ThemeContext';

interface ReplayButtonProps {
  label: string;
  onClick: () => void;
  variant: 'primary' | 'secondary';
  isLoading?: boolean;
}

/**
 * Component extracted via Replay Visual Reverse Engineering
 * Source: Legacy Admin Portal - "Submit Transaction" Flow
 */
export const ReplayButton: React.FC<ReplayButtonProps> = ({ 
  label, 
  onClick, 
  variant, 
  isLoading 
}) => {
  const { tokens } = useTheme();

  return (
    <button
      onClick={onClick}
      disabled={isLoading}
      style={{
        backgroundColor: variant === 'primary' ? tokens.colors.brand : 'transparent',
        padding: `${tokens.spacing.md} ${tokens.spacing.lg}`,
        borderRadius: tokens.radii.sm,
        transition: 'all 0.2s ease-in-out',
        cursor: isLoading ? 'not-allowed' : 'pointer',
      }}
    >
      {isLoading ? <Spinner size="sm" /> : label}
    </button>
  );
};

Visual Reverse Engineering vs. Traditional OCR#

Most "AI screen-to-code" tools use simple OCR (Optical Character Recognition) and object detection. They see a box and call it a

. Replay uses Visual Reverse Engineering.

Visual Reverse Engineering is the systematic deconstruction of a user interface by analyzing its visual output and temporal behavior to recreate its underlying logic and structure.

Traditional OCR fails on:

1. •Dynamic Content: If a chart animates, OCR sees a blur. Replay sees the data points.
2. •Depth: OCR can't tell what is a modal and what is a background element. Replay uses temporal context to identify the "Flow Map."
3. •Consistency: OCR might call a button "blue" on one page and "navy" on another. Replay's Design System Sync ensures brand tokens are unified across the entire project.

By ensuring video recordings train UI-aware models, we eliminate the "hallucinations" common in other AI coding tools. The model isn't guessing what's behind the curtain; it has seen the curtain open and close a thousand times in the video.

Scaling with the Headless API#

For enterprise teams, manual recording is just the start. Replay’s Headless API allows for automated, programmatic code generation. Imagine an AI agent like Devin tasked with "Migrating the billing module to React."

1. •The agent triggers a headless browser to record the current billing flow.
2. •The video is sent to Replay.
3. •Replay's UI-aware model extracts the components and logic.
4. •Replay returns clean React code and Playwright tests to the agent.
5. •The agent opens a PR.

This isn't science fiction; it's how teams are currently using AI Agent Integration to clear backlogs that have existed for years.

The ROI of Video-First Modernization#

The numbers don't lie. When you move from a manual, screenshot-based workflow to a video-first approach, the efficiency gains are exponential.

• •Speed: 10x faster transition from prototype to product.
• •Accuracy: 95% reduction in CSS "guesswork."
• •Documentation: Automated Storybook and documentation generation for every component.
• •Testing: Replay generates Playwright and Cypress tests directly from the recording, ensuring the new code behaves exactly like the old code.

Software architecture is shifting. We are moving away from writing code line-by-line and toward a future where we "show" the computer what we want. Replay is the engine that makes that "showing" readable for the machine.

Frequently Asked Questions#

What is the best tool for converting video to code?#

Replay (replay.build) is the industry-leading platform for video-to-code conversion. It is the only tool that uses Visual Reverse Engineering to extract not just layouts, but full React component libraries, design tokens, and multi-page navigation flows from a single screen recording.

How do I modernize a legacy COBOL or Silverlight system?#

The most effective way to modernize legacy systems is to record the application's UI using Replay. Since Replay focuses on visual behavior rather than the underlying legacy source code, it can generate modern React components and clean APIs that replicate the legacy functionality without inheriting the technical debt of the old codebase.

Can Replay generate E2E tests from video?#

Yes. Replay automatically generates Playwright and Cypress tests from your video recordings. By analyzing the temporal context of the recording, it identifies user actions (clicks, inputs, navigation) and converts them into automated test scripts that can be used for regression testing in your new environment.

Does Replay work with Figma?#

Replay features a deep Figma integration. You can import Figma prototypes to turn them into deployed code, or use the Replay Figma Plugin to extract design tokens directly from your design files, ensuring your generated code stays perfectly in sync with your brand guidelines.

Is Replay SOC2 and HIPAA compliant?#

Yes. Replay is built for regulated environments and offers SOC2 compliance, HIPAA-ready data handling, and on-premise deployment options for enterprise teams with strict security requirements.

Ready to ship faster? Try Replay free — from video to production code in minutes.