Enhancing Content Quality: Validating AI-Generated Posts

Introduction

Ensuring the quality of AI-generated content is crucial before it reaches the end user. This post details how we implemented content validation to detect and prevent truncated or incomplete AI-generated posts from being published in the devlog-ist/landing project.

The Problem: Truncated AI Output

AI models, especially when generating longer content, can sometimes be cut off due to token limits or other constraints. This results in incomplete posts with abrupt endings or unclosed code blocks, which degrades the user experience.

The Solution: Post-Generation Validation

To address this, we implemented a validation layer that runs immediately after content generation but before persisting the data. This validation checks for several key issues:

1. Prism finishReason: Checks if the AI's finishReason indicates truncation due to token limits.
2. Minimum Word Count: Enforces a minimum word count to ensure sufficient content length.
3. Unclosed Code Blocks: Detects and flags any unclosed code blocks, which are a common sign of truncation.

Implementation

The validation process involves these key steps:

try {
    $content = $aiService->generateContent();

    $this->validateContent($content);

    $this->postRepository->save($content);

} catch (ContentTruncatedException $e) {
    // Handle the exception, e.g., retry content generation or notify the user.
    report($e);
}

private function validateContent(string $content): void
{
    if ($this->isContentTruncated($content)) {
        throw new ContentTruncatedException('AI-generated content is truncated.');
    }
}

private function isContentTruncated(string $content): bool
{
    // Implement checks for finishReason, minimum word count, and unclosed code blocks
    // Return true if any of the checks fail
}

Benefits

• Improved Content Quality: Prevents the publication of incomplete or broken posts.
• Enhanced User Experience: Ensures users receive complete and coherent information.
• Reduced Manual Review: Automates the detection of common AI output issues, reducing the need for manual review.

Next Steps

Consider expanding the validation rules to include checks for grammar, readability, and factual accuracy. Implement automated retries or alerts when content is flagged as truncated to further streamline the content creation process.