As AI tool usage has become more common, I’ve seen impressive examples of people building tools to automate complex processes that once required significant manual effort. I’ve also seen teams adopt AI simply because it’s available, often with little practical benefit.
My approach is to focus on AI applications that save time and solve real problems.
Recently, I needed to align the SEO architecture for more than a dozen websites across three separate businesses, eight regional domains, and multiple languages, including three English dialects, Italian, Japanese, Spanish, Thai, French, and Korean.
Historically, mapping thousands of URLs to create cohesive hreflang XML sitemaps would have required specialized software or days of spreadsheet work. Instead, I used Google Gemini to build a custom Python script that handled the heavy lifting.
Here’s how the project evolved from an initial prompt into a highly customized automation tool, and what it taught me about using AI for technical SEO.
Where AI delivers the most value
I use AI primarily for practical, time-saving tasks, including:
- Generating regex patterns when I need a quick solution without researching syntax from scratch.
- Creating complex spreadsheet formulas for reporting workflows that rely on manual data exports.
- Accelerating research and planning for projects that require competitive analysis across multiple business lines.
- Building custom automation tools for recurring SEO and data-processing tasks.
The hreflang project discussed here falls into that final category.
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Mapping hreflang at scale
The challenge was clear: map thousands of URLs across more than a dozen multilingual websites into accurate hreflang XML sitemaps.
Rather than tackling the project manually, I used Google Gemini to help build a custom Python solution.
Here’s how the process unfolded.
Phase 1: Asking for an approach, not just a script
A common pitfall when using generative AI for coding is asking it to sprint before it knows the route. If you simply type, “Write a Python script to create an hreflang sitemap,” you’ll get a generic, fragile piece of code that breaks the moment it encounters real-world data.
Instead, I started by asking for an approach. I explained the scenario: multiple regional domains, organic growth over several years resulting in mismatched URL slugs, translated subfolders, and appended revision years.
Gemini suggested a multi-step, data-driven approach:
- Crawl the websites to collect live URLs and their metadata.
- Use Python in Google Colab to process the raw data.
- Run an exact match cluster first to group identical slugs.
- Use…
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