Automating Project Documentation with AI Browser Automation

One of the challenges of maintaining a portfolio is keeping project documentation comprehensive, consistent, and up-to-date. I've developed an AI-powered workflow that uses browser automation to systematically review websites and generate structured project data that seamlessly integrates with my Next.js portfolio.

The Challenge

Creating detailed project documentation typically involves:

• Manual Screenshots - Capturing multiple views at consistent dimensions
• Information Gathering - Extracting technical details, descriptions, and metadata
• Content Writing - Crafting engaging project descriptions
• Data Structuring - Formatting everything consistently for the portfolio

This process is time-consuming and prone to inconsistencies, especially when documenting multiple projects.

The Solution: AI-Powered Browser Automation

I've created a comprehensive prompt that guides AI agents through systematically reviewing websites using Playwright browser automation. The AI:

1. Navigates the website - Explores pages to understand the full scope
2. Captures screenshots - Takes consistent, high-quality images
3. Analyzes the tech stack - Identifies frameworks and technologies
4. Extracts content - Gathers descriptions and key information
5. Generates structured data - Creates JSON files ready for the portfolio

How It Works

The Project Review Prompt

The prompt acts as a detailed instruction manual for the AI agent. It defines:

• Browser automation workflow - Step-by-step navigation instructions
• Data collection requirements - What information to gather
• Quality standards - Ensuring consistency and completeness
• Output format - JSON structure matching the TypeScript interface

Here's the workflow the AI follows:

1. Start browser → Navigate to URL → Wait for load
2. Take full-page screenshot (featured image)
3. Navigate to different sections/pages
4. Capture targeted feature screenshots
5. Analyze tech stack via console and inspection
6. Extract text content and metadata
7. Generate comprehensive JSON file

The TypeScript Interface

The portfolio expects project data in a specific structure:

interface Project {
  title: string;
  description: string;
  longDescription?: string;
  tags: string[];
  featured?: boolean;
  image?: string;
  images?: string[];
  demoUrl?: string;
  date: string;
  status: 'completed' | 'in-progress' | 'archived';
  collaboration?: {
    type: 'human' | 'ai' | 'hybrid';
    details?: string;
  };
}

The Dynamic Project Page

The project page component (app/(public)/projects/[slug]/page.tsx) automatically:

• Generates static pages - Pre-renders all projects at build time
• Creates metadata - SEO-optimized titles and descriptions
• Adds structured data - Schema.org JSON-LD for search engines
• Renders rich content - Displays all project details beautifully

The Benefits

This AI-powered approach provides several advantages:

Consistency

Every project is documented with the same thoroughness and structure. No more forgotten fields or inconsistent formatting.

Speed

What previously took 30-60 minutes per project now takes less than 5 minutes. The AI can review a website and generate complete documentation faster than I could write the description alone.

Comprehensiveness

The AI systematically explores the entire website, often finding details I might have missed. It checks meta tags, console messages, and page structure for technical insights.

Quality

The prompt includes quality checks and writing guidelines, ensuring professional descriptions and accurate technical details.

Real-World Implementation

When I want to add a new project to my portfolio, I simply:

1. Provide the website URL to the AI agent
2. The AI reviews the site using browser automation
3. I review and approve the generated content
4. The JSON file is saved to content/projects/
5. The portfolio automatically renders the new project page

The collaboration between human oversight and AI automation ensures accuracy while maximizing efficiency.

Human-AI Collaboration

This workflow exemplifies effective human-AI collaboration:

• Human provides: Strategy, quality standards, and final approval
• AI provides: Systematic execution, data extraction, and content generation
• Together: We achieve better results faster than either could alone

Looking Forward

This approach to project documentation is just the beginning. Future enhancements could include:

• Automated updates - Periodically checking projects for changes
• Performance metrics - Capturing lighthouse scores and load times
• Interactive previews - Recording video demonstrations
• Cross-referencing - Linking related projects and blog posts

Conclusion

By combining AI capabilities with browser automation, I've transformed project documentation from a tedious manual process into an efficient, systematic workflow. This not only saves time but ensures every project in my portfolio receives the detailed documentation it deserves.

The prompt serves as a reusable blueprint, making it easy to maintain consistency across all projects while adapting to each project's unique characteristics. It's a perfect example of how AI can augment human capabilities in practical, productive ways.

This post demonstrates how AI-powered automation can streamline documentation workflows while maintaining high quality standards.