Retrieval-augmented generation
Jan 27, 2024 23:47:51 GMT -5
Post by account_disabled on Jan 27, 2024 23:47:51 GMT -5
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? (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.
Retrieval-augmented generation (RAG) While the market DB to Data Microsoft introduced this innovation with the new Bing, the Facebook AI Research team first published the concept in May 2020 in the paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” presented at the NeurIPS conference. However, Neeva was the first to implement this in a major public search engine by having it power its impressive and highly specific featured snippets. This paradigm is game-changing because, although LLMs can memorize facts, they are “information-locked” based on their training data.
For example, ChatGPT’s information has historically been limited to a September 2021 information cutoff. The RAG model allows new information to be considered to improve the output. This is what you’re doing when using the Bing Search functionality or live crawling in a ChatGPT plugin like AIPRM. This paradigm is also the best approach to using LLMs to generate stronger content output. I expect more will follow what we’re doing at my agency when they generate content for their clients as the knowledge of the approach becomes more commonplace.
Retrieval-augmented generation (RAG) While the market DB to Data Microsoft introduced this innovation with the new Bing, the Facebook AI Research team first published the concept in May 2020 in the paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” presented at the NeurIPS conference. However, Neeva was the first to implement this in a major public search engine by having it power its impressive and highly specific featured snippets. This paradigm is game-changing because, although LLMs can memorize facts, they are “information-locked” based on their training data.
For example, ChatGPT’s information has historically been limited to a September 2021 information cutoff. The RAG model allows new information to be considered to improve the output. This is what you’re doing when using the Bing Search functionality or live crawling in a ChatGPT plugin like AIPRM. This paradigm is also the best approach to using LLMs to generate stronger content output. I expect more will follow what we’re doing at my agency when they generate content for their clients as the knowledge of the approach becomes more commonplace.