A deep dive into superior indexing, pre-retrieval, retrieval, and post-retrieval strategies to reinforce RAG efficiency
Have you ever requested a generative AI app, like ChatGPT, a query and located the reply incomplete, outdated, or simply plain fallacious? What if there was a solution to repair this and make AI extra correct? There may be! It’s known as Retrieval Augmented Technology or simply RAG. A novel idea launched by Lewis et al of their seminal paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , RAG has swiftly emerged as a cornerstone, enhancing reliability and trustworthiness within the outputs from Massive Language Fashions (LLMs). LLMs have been proven to retailer factual information of their parameters, additionally known as parametric reminiscence and this data is rooted within the information the LLM has been skilled on. RAG enhances the information of the LLMs by giving them entry to an exterior info retailer, or a information base. This information base can also be known as non-parametric reminiscence (as a result of it’s not saved in mannequin parameters). In 2024, RAG is one of the most widely used techniques in generative AI applications.
60% of LLM purposes make the most of some type of RAG