Retrieval-Augmented Generation Explained Consider a sports league that wants fans and the media to be able to use chat to access its data and answer questions about players, teams, the sport’s history and rules, and current stats and standings. A generalized LLM could answer questions about th...
Retrieval-augmented generation (RAG) enables organizations to deploy customized LLM applications quickly and cost-effectively.
If the concept of retrieval-augmented generation (RAG) has piqued your interest, diving into its technical implementation will offer invaluable insights. With large language models (LLMs) as the backbone, RAG employs intricate processes, from data sourcing to the final output. Let's peel back the...
This is where retrieval augmented generation (RAG) comes in. Broadly speaking, RAG is a method for giving AI models access to additional external information that they haven't been trained on. Crucially, it allows AI models to access new and up-to-date information without needing to be retrai...
Retrieval-augmented generation (RAG) is an AI framework that retrieves data from external sources of knowledge to improve the quality of responses. This natural language processing (NLP) technique is commonly used to make large language models (LLMs) more accurate and up to date. ...
So, in the spring of 2020, Kiela and his team published aseminal paperof their own, which introduced the world toretrieval-augmented generation. RAG, as it’s commonly called, is a method for continuously and cost-effectively updating foundation models with new, relevant information, including ...
A video tutorial explaining retrieval augmented generation. Credit: IBM Technology / YouTube There are all kinds of clever search techniques to make this work better, but the general idea is “find the relevant information.” This is the retrieval step of RAG, and it happens before the LLM ...
Language models have shown impressive capabilities. But that doesn’t mean they’re without faults, as anyone who has witnessed a ChatGPT “hallucination” can attest. Retrieval Augmented Generation (RAG) is a framework designed to make language models more reliable by pulling in relevant, up-to...
GenAI has exploded and so too has the need for efficient data management and retrieval systems, leveraging vector databases and software-defined storage to enhance the capabilities of Retrieval Augmented Generation (RAG) models. When I asked Google and ChatGPT “What does RAG stand for?”, both ...
Discover the potential of Retrieval Augmented Generation in GenAI systems. Find out how RAG improves response accuracy and expands knowledge.