What do golden retrievers have to do with AI?
Not as much as pre-trained neural retrievers do!
In my last post, I introduced the concept of Retrieval-Augmented Generation (RAG), a powerful technique that combines the strengths of retrieval systems with generative language models to produce more accurate and up-to-date responses.
The retriever is a crucial component of a RAG system. Unlike a traditional search engine that matches keywords to web pages, a retriever is a specialized neural network trained to understand the semantic relationship between a user’s query and a vast amount of pre-indexed documents or passages.
The retriever relies on its parameterized memory, which means it has learned from vast amounts of query-document pairs during training. This training allows it to match new, unseen queries to the most relevant passages it has indexed, providing targeted and context-rich information to the language model.
Rather than fetching information from live web pages, which could be slow and unreliable, the retriever works with a curated, pre-indexed dataset. This dataset is regularly updated to ensure it includes fresh, high-quality information while maintaining the speed and reliability of retrieval.
In my next post I will consider some of the security concerns that come with using RAG.
What security concerns do you have about using AI?
#AI #RAG
