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Pablo Navascués, Managing Director of global content and SEO agency Key Content, joins SlatorPod to discuss the impact of generative AI on content creation and SEO.
Pablo explains how content strategy and SEO strategy have become more interconnected, emphasizing the need for high-quality, user-oriented content. He acknowledges that AI can be valuable for scaling content production. However, he highlights the need for human expertise in certain cases, as AI-generated content may not always meet quality and accuracy standards.
When it comes to Google's approach to ranking AI-generated content, Pablo suggests that Google values content based on how helpful it is to users rather than focusing on whether it is created by humans or AI.
Pablo talks about the challenges of prompt engineering in working with large language models and how it can be utilized for A/B testing and data analysis.
He shares insights from their research on AI content tools, which revealed that there is no one-size-fits-all solution, and different tools perform better depending on the type of content, audience, and language.
Pablo predicts that content workflows will become more sophisticated and personalized, and the role of content strategists within organizations will become more critical.
By Slator4.3
66 ratings
Pablo Navascués, Managing Director of global content and SEO agency Key Content, joins SlatorPod to discuss the impact of generative AI on content creation and SEO.
Pablo explains how content strategy and SEO strategy have become more interconnected, emphasizing the need for high-quality, user-oriented content. He acknowledges that AI can be valuable for scaling content production. However, he highlights the need for human expertise in certain cases, as AI-generated content may not always meet quality and accuracy standards.
When it comes to Google's approach to ranking AI-generated content, Pablo suggests that Google values content based on how helpful it is to users rather than focusing on whether it is created by humans or AI.
Pablo talks about the challenges of prompt engineering in working with large language models and how it can be utilized for A/B testing and data analysis.
He shares insights from their research on AI content tools, which revealed that there is no one-size-fits-all solution, and different tools perform better depending on the type of content, audience, and language.
Pablo predicts that content workflows will become more sophisticated and personalized, and the role of content strategists within organizations will become more critical.

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