How will the relationship between structured data and generative AI evolve over the next 18 months?
episode
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
5 min
2 speakers
6 chapters
transcribed 29 days ago
Transcript
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Transcript generated automatically by AI and may contain errors.
What is the overall focus of today’s episode on structured data and generative AI?
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looking into the future
now, Martha. Tell me how the relationship between structured data and generative AI will evolve over the next eighteen months.
I think this is why MCPU is such an interesting topic.
How will structured data become the primary data feed for large language models?
Like I think we're gonna basically use structured data as the feed into the models and it's going to become like even less of an S like just an SEO thing and it's gonna become like this is like your data strategy for LLMs. But we're gonna have to just like journey on that. Like I would say, like we're still pretty early days in that. I'm excited because we're gonna we're gonna just like make that happen.
Absolutely. I agree. Frameworks and protocols are going to be a way for us to organize and structure better because none of these none of these models and we don't know if these models will verticalize themselves or they'll become super critical for certain types of consumers. As that happens, we have to be very prudent about what we supply. And and I think these frameworks and models are going to be critical over the next 18 months.
I think it's going to be data feeds with context.
Why is context‑rich structured data essential for reducing AI hallucinations?
And and I I say that very specifically because I don't know, I read like four articles this week where it's like, you lm.txt, like that's not going to be good enough, right? The reason why all the research says knowledge graphs and large language models work well is because it's data with context. So it's structured data. Think that broad concept. But it has context, which means you're defining relationships between things. Because it's when those relationships are defined that you can do inferencing. And the problem we're solving for large language models is the inferencing part. That's the expensive part, right?
What role will frameworks and protocols play in organizing data for LLMs?
It's the part that hallucinates. And so I think like historically, if you'd asked me a year ago, I'd be like, Enterprise are going to be investing in knowledge graphs. I think like as we have new standards, like that's hard to do and we're seeing people still do that work. But I think the most important, the reason why knowledge graphs are important, because it's data with context.
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Chapters
6 chapters
1
What is the overall focus of today’s episode on structured data and generative AI?
0:00–0:58
2
How will structured data become the primary data feed for large language models?
0:58–1:50
3
Why is context‑rich structured data essential for reducing AI hallucinations?
1:50–2:22
4
What role will frameworks and protocols play in organizing data for LLMs?
2:22–3:31
5
How are knowledge graphs expected to evolve as a strategic asset for enterprises?
3:31–4:15
6
What are the key takeaways and next steps for listeners interested in data‑driven AI?
4:15–4:48