At small volumes, manual review is feasible. At hundreds or thousands of texts per month, it is not. Yet you still want to know:

• Is the text long enough?
• Does it contain the required keywords?
• Is the tone consistent with the brand?
• Are any prohibited words or claims included?
• Is the structure as expected?

For all these questions you can build automatic checks that run immediately after generation. Texts that do not pass the checks are flagged for human review or automatically regenerated.

There are several categories of automatic checks:

Structural checks:

• Text falls within the desired word count (min/max)
• Required sections or headings are present
• CTA is included

Content checks:

• Primary keyword is present
• Prohibited terms or claims are not included
• Product names are spelled correctly
• No placeholder text remains

Style checks:

• Readability score (Flesch-Kincaid or similar) within desired range
• Average sentence length suits the target audience
• No excessive use of superlatives or vague claims

Technical checks:

• Links are valid
• No duplicate content (comparison with existing pages)
• Correct formatting (no broken markdown or HTML)

Automatic checks are implemented as part of the content pipeline:

1. After generation: The AI output is immediately run through a validation script
2. Scoring: Each check produces a pass/fail or a score
3. Thresholds: Decide which checks block (hard limit) and which only trigger a notification (soft limit)
4. Reprocessing: Flagged texts are regenerated or passed to human review
5. Logging: All validation results are stored for monitoring

Most checks can be implemented in Python using regular expressions, NLP libraries or API calls to external services for readability or plagiarism detection.

Automatic validation has clear limits:

Factual accuracy: A check can verify that a number is present, but not that it is correct. Factual verification remains human work.

Brand feel: Whether a text truly sounds like your brand is difficult to quantify. Classification models can help, but are not infallible.

Contextual logic: A text can be grammatically correct and contain all required elements while still being internally contradictory. Automatic checks will not catch that.

New error types: If AI makes a new kind of error you have not yet anticipated in your checks, it will slip through.

Automatic validation does not replace all human review, but it reduces it considerably. A good approach combines:

• Automatic checks for all output
• Human review for flagged texts
• Random review of a fixed percentage (e.g. 5-10%) of non-flagged texts
• Periodic audits of published content

Mach8 designs quality systems like this for clients who want to set up AI content production at scale without sacrificing reliability.

Validation systems are not a finished product. They improve as you learn more about the errors AI makes in your specific context. Track which types of errors occur most frequently and add targeted checks for them.

The prompts themselves also improve through validation data: if a certain type of check consistently fails, that is a signal the prompt needs adjustment.

Automatic validation is the backbone of reliable AI content production at scale. It does not provide a complete guarantee of quality, but it makes it possible to process large volumes with a manageable level of oversight.

Want to build a validation system for your AI content workflow? View our content production services or get in touch with Mach8.