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🗓 Wednesday 18th March • 5 pm GMT / 12 pm ET / 9 am PT
Publishing AI content without testing is a gamble with your rankings.
Most teams are publishing AI content and hoping for the best. This webinar gives you a proven testing roadmap, so you know exactly what's working before you scale.
AI content generation models have changed dramatically in just the last two years, and so have search engines' ability to evaluate content quality.
The question is no longer "can AI content improve traffic?" We know it can. The question is which AI content, generated with which model, which prompt, and deployed in which way.
This session walks through a testing roadmap that answers all of them, with real experiments you can run on your own site.
✦ Free tool included ✦
Registrants get exclusive access to the Traffic Trade-off Calculator - an interactive tool that shows exactly what it takes to replace lost organic traffic with LLM discovery traffic..png?width=700&height=225&name=Thumbnail%20(18).png)
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How to know if your AI content is actually worth keeping
Before you invest in generating more AI content, you need to verify the content you already have is pulling its weight. We'll show you a simple test series, remove, hide, relocate, that reveals whether a content element is boosting or quietly dragging your rankings.
The right LLM for your content isn't the most popular one
OpenAI and Gemini get all the press, but they may not be the best fit for your specific content needs. We'll walk through a practical framework for comparing models head-to-head using real SEO data, so you can stop guessing and start generating content that actually ranks.
Small prompt changes can make or break your organic performance
Two prompts fed into the same model can produce wildly different SEO results. You'll see concrete test ideas, from keyword-specific prompts to using your existing content as training input, that reveal which prompt strategy drives the most organic traffic lift.
Why AI translation is riskier than you think (and how to test it safely)
Machine translation isn't all the same, SMT, NMT, and LLM models handle your content very differently across languages. We'll reveal the pitfalls teams fall into when scaling multilingual content with AI, and share a testing methodology to ensure quality and accuracy across every market you operate in.