SEO Audit with AI: 56 Posts, One Afternoon

I've been putting off an SEO audit of my blog for months. With 30+ articles and 20+ code snippets, the task felt overwhelming. Reviewing each meta description, checking for broken links, validating alt text — it's the kind of work that's easy to procrastinate on indefinitely.

Then I tried something different: I used AI as my editorial assistant. In a single afternoon, I audited and improved 56 files. Here's how it went.

The Boring Work Nobody Wants to Do

Let's be honest — SEO maintenance is boring. It's not the exciting part of running a developer blog. You know you should do it, but there's always something more interesting to work on.

My blog had accumulated typical SEO debt over time. Generic meta descriptions that said nothing specific, like "Learn about X" instead of actual summaries. Broken external links because MDN reorganized their docs and Git changed their download URL. Empty alt attributes on images where I'd rushed through posts and left alt="". The usual mess.

Fixing this manually would mean opening each file, reading the content, rewriting descriptions, and validating every link. For 56 files? That's a full weekend I'd never actually spend.

Teaching AI What I Need

The key insight was treating AI not as a magic "fix everything" button, but as a focused assistant with clear constraints. I use Cursor as my editor, and it supports project-specific rules — markdown files that act as persistent instructions for the AI.

I created a rules file (.cursor/rules/seo.mdc) that defined exactly what I needed:

SEO responsibilities:
- Propose 2–5 realistic, developer-focused keyword phrases for each article
- Suggest an SEO-friendly title (50-60 characters) and meta description (110-160 characters)
- Naturally integrate the main keyword into title, first paragraph, and conclusion
- Never sacrifice clarity or honesty for SEO

This file acts as persistent context. Every time the AI reviews an article, it knows who I am (a senior software engineer), who my audience is (developers from junior to senior level), my writing style (conversational, direct, technical), and what to avoid (inventing experience, adding buzzwords, breaking code).

That last constraint — "never sacrifice clarity for SEO" — is crucial. I've seen AI-generated content stuffed with keywords that reads like garbage. That's not what I wanted.

What the SEO Audit Found

Before:

description: Polymorphism and Dispatch Tables in JS

After:

description: Replace messy switch statements with dispatch tables in JavaScript. 
Learn this clean code pattern for handling multiple conditions with maintainable code.

The first one tells you nothing. The second explains the benefit, uses action words, and includes relevant keywords naturally.

MDN restructured their documentation URLs at some point too. Links that worked a year ago now 404. The AI flagged these:

- [CSS Grid](https://developer.mozilla.org/en-US/docs/Web/CSS/CSS_grid_layout)
+ [CSS Grid](https://developer.mozilla.org/en-US/docs/Web/CSS/Guides/Grid_layout)

Same for Flexbox and other external resources. Small fixes, but broken links hurt both SEO and user experience.

Then there was alt text. In my React re-renders article, I had multiple images with empty alt attributes:

- <Image alt="" src="rerender.png" />
+ <Image alt="Diagram showing React component tree re-rendering when state updates" src="rerender.png" />

The AI suggested appropriate descriptions based on the surrounding content in seconds.

Wrapping Up

The goal here isn't to automate away quality control. It's to make the tedious parts fast enough that you actually do them.

By the end of the afternoon: 56 files updated, 50+ meta descriptions rewritten, 6 broken links fixed, 12 empty alt attributes filled.