Search, as we know it, has been irrevocably changed by generative AI.
The rapid improvements in Google’s Search Generative Experience (SGE) and Sundar Pichai’s recent proclamations about its future suggest it’s here to stay.
The dramatic change in how information is considered and surfaced threatens how the search channel (both paid and organic) performs and all businesses that monetize their content. This is a discussion of the nature of that threat.
While writing “The Science of SEO,” I’ve continued to dig deep into the technology behind search. The overlap between generative AI and modern information retrieval is a circle, not a Venn diagram.
The advancements in natural language processing (NLP) that started with improving search have given us Transformer-based large language models (LLMs). LLMs have allowed us to extrapolate content in response to queries based on data from search results.
Let’s talk about how it all works and where the SEO skillset evolves to account for it.
What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) is a paradigm wherein relevant documents or data points are collected based on a query or prompt and appended as a few-shot prompt to fine-tune the response from the language model.
It’s a mechanism by which a language model can be “grounded” in facts or learn from existing content to produce a more relevant output with a lower likelihood of hallucination.
While the market thinks Microsoft introduced this innovation with...
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