I used LLMs to rewrite meta descriptions for 1,600 articles — honest results

Meta descriptions are the most underrated SEO element on content-heavy sites.

They don't affect rankings directly, but they determine whether someone clicks your result in Google. A bad meta description on a well-ranked article is traffic you're leaving on the table.

I had , many descriptions ended up following the same pattern:

"Learn how to [verb] [AD concept] to protect your environment from [threat]. Step-by-step guide with [tool]."

Technically valid. Practically, if someone searches and sees 5 results from the same site with near-identical descriptions, they'll click none of them.

Fix: I added a deduplication check that compares new descriptions against already-generated ones using simple n-gram similarity. If similarity > 0.7, force a regeneration with an explicit instruction to use a different structure.

Things I'd do differently

1. Fix excerpts before running LLM generation

The quality of the generated description is directly proportional to the quality of the excerpt. I should have audited and fixed all excerpts first. I did it in the wrong order.

2. Category-specific prompts

A prompt for a "news" article should be different from a "guide" or "checklist" article. News descriptions need urgency; guides need the benefit; checklists need the scope. I used one prompt for everything and paid for it in quality.

3. Track CTR per article, not just aggregate

I know the average improved, but I don't know which specific articles drove the improvement. Better instrumentation would let me learn which description styles work for which query intents.

The actual takeaway

LLMs are genuinely useful for this kind of bulk text generation task if you:

1. Write a tight prompt with hard constraints


2. Build validation + retry logic (don't trust the model to self-validate)


3. Have decent source material to work from


4. Measure the actual downstream metric (CTR), not a proxy

They're not useful if you expect them to compensate for bad content strategy. Garbage in, slightly better-formatted garbage out.

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