Content Validation: Guarding Against Truncated AI Output

In the devlog-ist/landing project, we're focused on delivering high-quality content. A crucial part of this is ensuring that AI-generated content meets our standards before it's published.

The Problem: Silent Content Truncation

AI models, particularly when generating longer pieces of content, can sometimes be cut short due to token limits or other constraints. This can result in incomplete or nonsensical posts being saved without any immediate indication of the issue. We needed a way to automatically detect and prevent this.

The Solution: Post-Generation Validation

To address this, we've implemented a post-generation content validation process. This validation occurs before the AI-generated content is persisted in the system. The validation checks for the following:

1. Prism finishReason: We inspect the finishReason property returned by the AI model. If it indicates token-limit truncation, the content is flagged as incomplete.
2. Minimum Word Count: We enforce a minimum word count to ensure that the generated content has sufficient substance.
3. Unclosed Code Blocks: We detect and flag any unclosed code blocks, which are a common symptom of premature truncation.

Implementation Details

The validation logic is encapsulated in a new service that throws a ContentTruncatedException when any of the validation checks fail. This exception prevents the broken post from being silently saved or published.

Here's a simplified example of how the validation process might look:

def validate_content(content):
    if is_truncated(content):
        raise ContentTruncatedException("Content was truncated by the AI model.")
    if len(content.split()) < MIN_WORD_COUNT:
        raise ContentTruncatedException("Content does not meet minimum word count.")
    if has_unclosed_code_blocks(content):
        raise ContentTruncatedException("Content has unclosed code blocks.")
    return True

This validate_content function performs our core checks. If any check fails, a ContentTruncatedException is raised, preventing the invalid content from being used.

The Benefits

By implementing post-generation content validation, we ensure that only complete, high-quality AI-generated content is published. This prevents the silent introduction of truncated or incomplete posts, improving the overall quality and reliability of our content.

Actionable Takeaway

Consider implementing similar validation checks in your own AI-powered content generation workflows. By validating the output before persisting it, you can catch and prevent issues early on, ensuring the quality and reliability of your content.