Behind the Scenes: Our Automated Content Generation Process

At AI Tools Directory, we’re not just cataloging AI tools – we’re using them to power our own content creation process. In this post, we’ll take you behind the scenes of our automated content generation pipeline, showcasing how we use a combination of cutting-edge tools to streamline our workflow from initial news curation to multilingual post deployment.

Our Toolkit

Before we dive into the process, let’s quickly review the key tools in our arsenal:

1. n8n: Our automation backbone, orchestrating the entire workflow.
2. Baserow: Our database for storing and managing content data.
3. ChatGPT and Claude: AI models for content generation and rewriting.
4. Google Drive: For storing generated images.
5. GitHub: For version control and triggering our deployment process.
6. Hugo: Our static site generator.
7. Cloudflare: For hosting our preview versions.

The Content Generation Flow

Our process is divided into three main flows, each handled by n8n. Here’s a step-by-step breakdown:

Flow 1: News Curation and Initial Processing

1. Manual Input: We start by manually adding links to interesting news articles in a Baserow table.
2. Content Retrieval: n8n triggers a workflow that scrapes the content from these links.
3. Data Storage: The retrieved content is stored in a second Baserow table for processing.

Flow 2: Content Rewriting and Image Generation

1. Content Rewriting: n8n calls either ChatGPT or Claude to rewrite the article, adding our specific context and background.
2. Image Generation: The flow then generates a relevant image for the article.
3. Image Storage: The generated image is stored on Google Drive.
4. Data Update: The rewritten content and image link are stored back in Baserow.

Flow 3: Multilingual Post Generation and Deployment

1. Translation: n8n triggers a workflow to translate the rewritten content into multiple languages.
2. Hugo Post Creation: The flow generates Hugo-compatible markdown files for each language version.
3. GitHub Integration: A new branch is created on GitHub with the generated posts.
4. Pull Request: A pull request is automatically created with the changes.
5. Preview Deployment: The pull request triggers a preview build on Cloudflare.

Future Enhancements

We’re constantly looking to improve our automation process. Here are some planned enhancements:

1. Telegram Notifications: Implement a human-in-the-loop system for final approval and modifications.
2. Tool Insertion Automation: Streamline the process of adding new AI tools to our directory.
3. Data Extraction Optimization: Automate the extraction of data, URLs, and images for tools.
4. Property Streamlining: Develop a system to automatically extract and standardize properties for each tool.

The Power of Automation in Content Creation

By leveraging this automated workflow, we’re able to:

1. Rapidly respond to new developments in the AI tool landscape.
2. Maintain consistency across our multilingual content.
3. Ensure our directory stays up-to-date with minimal manual intervention.
4. Focus our human efforts on high-level curation and strategy rather than repetitive tasks.

Conclusion

Our automated content generation process is a testament to the power of AI and automation tools in modern content creation. By combining n8n, Baserow, AI language models, and a suite of development tools, we’ve created a robust pipeline that allows us to maintain a cutting-edge, multilingual AI tool directory with efficiency and scale.

As we continue to refine this process, we’re excited about the possibilities it opens up for not just our project, but for the future of content creation and curation in the rapidly evolving world of AI tools.

Stay tuned for more updates on our automation journey, and don’t forget to explore the latest AI tools in our ever-growing directory!