Enhancing Content Quality: Automated Validation for AI-Generated Posts

Introduction

Ensuring the quality of AI-generated content is crucial before it reaches the end user. This post details how to implement automated validation checks to detect and prevent the publication of truncated or incomplete AI-generated articles in the devlog-ist/landing project.

The Challenge

AI models, while powerful, can sometimes produce incomplete outputs due to token limits, unexpected interruptions, or formatting errors. Publishing such content can negatively impact user experience and credibility. We need a robust mechanism to catch these issues before they go live.

Solution: Post-Generation Validation

To address this, we've implemented a series of validation steps that run immediately after the AI generates content but before it's persisted to the database. These checks include:

1. Prism finishReason Check: Verifies that the AI model completed the generation process without hitting token limits. The finishReason property should indicate successful completion.
2. Minimum Word Count Enforcement: Ensures that the generated content meets a minimum length requirement, preventing the publication of excessively short or superficial articles.
3. Unclosed Code Block Detection: Identifies and flags any unclosed code blocks, which can disrupt the layout and readability of the post.

Implementation Details

Here's a simplified example of how you might implement the word count validation:

class ContentValidator
{
    public static function validateWordCount(string $content, int $minWords):
    bool
    {
        $wordCount = str_word_count($content);
        return $wordCount >= $minWords;
    }
}

if (!ContentValidator::validateWordCount($content, 150)) {
    throw new ContentTruncatedException('Content does not meet minimum word count.');
}

This ContentValidator class provides a method to check if the content meets the minimum word count requirement. If the validation fails, a ContentTruncatedException is thrown.

Handling Validation Failures

When a validation check fails, a ContentTruncatedException is thrown. This prevents the broken post from being silently saved or published. Instead, the system can log the error, notify the content team, and trigger a regeneration of the content.

Benefits

• Improved Content Quality: Prevents the publication of truncated or incomplete articles.
• Enhanced User Experience: Ensures that users receive complete and coherent content.
• Increased Credibility: Maintains the reputation of the platform by avoiding low-quality posts.
• Reduced Manual Review: Automates the detection of common content issues, freeing up human reviewers to focus on more complex tasks.

Conclusion

By implementing post-generation content validation, we can significantly improve the quality and reliability of AI-generated articles. This automated approach ensures that only complete and well-formed content is published, enhancing the overall user experience and maintaining the platform's credibility.