Three months ago, I was staring at a $4,200 monthly bill for API calls. We were using a large language model (LLM) to audit our client’s 50,000-page e-commerce site. The task? Identify broken internal links, missing alt tags, and thin content.

The LLM worked. But it hallucinated.

It flagged perfectly valid canonical tags as duplicates. It missed actual 404s because it couldn’t render JavaScript-heavy dynamic routes. The false positive rate was sitting at 18%. That meant my team spent 20 hours a week cleaning up the LLM’s mess.

I realized we were trying to solve a deterministic problem (finding broken links) with a probabilistic tool (generative AI).

I stopped the contract. I spun up a local instance of Llama 3 8B. I fine-tuned it on our own historical audit logs.

The result? Accuracy jumped to 94%. Costs dropped to $15. The inference time was faster, too, because the model was smaller and specialized.

This wasn’t magic. It was basic engineering. And it’s the reality check most SEOs need right now.

Large Models Are Overkill For Structured Tasks

Everyone thinks "AI" means "Big Model." You hear about trillion-parameter networks. You see headlines about AGI timelines.

But in SEO, we deal with structure. HTML tags are structured. URLs are structured. Schema markup is structured.

When you feed a billion-token generalist model a task that requires checking if `

` matches `