Retrieval augmented generation, abbreviated to RAG, allows large language models (LLMs) to fetch relevant information from specific data sources, augmenting the data the model was trained on.
Essentially RAG works by giving LLMs an open book resource when addressing tricky questions or queries that are heavily context dependent. If you think of the data set it was trained on as its long-term memory, providing a general understanding of how things work, RAG is the textbook for a specific problem.
The term was first coined in a 2020 paper on LLMs by an international team of researchers including experts working at tech giant Meta. Then, the long-term memory was an understanding of how language works, and the textbook was a collection of Wikipedia articles.
As generative AI has developed, though, the importance of RAG has only increased. Now it’s set to revolutionize the way much of the world works.
Programs that answer questions
Computers that answer questions are no longer the stuff of sci-fi, and maddening conversations with an uncomprehending Siri or Alexa will be a thing of the past.
RAG allows systems to fetch information from specific systems and stay updated beyond the LLMs training dataset.
This will allow AI to revolutionize industries like healthcare, where guidance is being constantly updated and effective responses need the latest information and where a personalized response makes all the difference.
Actionable analysis
Success in business often means successful data analysis. The better we can spot trends and patterns, the quicker we are to identify opportunities, and the better we can improve business processes.
RAG gives businesses the ability to leverage the latest market data and bring the immense power of generative AI to bear on chunks of business data that are as relevant as they are large.
That means more and more businesses will be working to develop systems that give them a cutting edge, using generative AI and RAG to analyze data in ever more inventive and optimized ways.
Accurate content creation and summarization
When GenAI fails at a content creation or summarization task, it’s usually because it’s failing to understand some important contextual information, or its data set is out of date.
But by using retrieval augmented generation, LLMs pull in accurate, current information from a number of sources and so can produce content that is far richer in terms of factual details.
This could spell the dawn of a new era in many creative industries, where new skills will be needed to work effectively alongside generative AI systems in producing content.
Legal Research
Legal research is vital work, but it is extremely time-consuming, and the body of knowledge clerks must search for is forever changing and expanding.
LLMs and generative AI can do this searching in a fraction of the time, but typically don’t have access to the right information.
RAG bridges that gap, making searches accurate and up to date.
Chat agents that follow the conversation
Most chatbots try to mimic a conversation but rarely get anywhere close.
That’s because they’re not really responding to your answers, they’re using keywords to decide which template response to show you next.
RAG allows LLMs to reference the user’s own real-time interaction with the chatbot to inform how it answers your question, potentially revolutionizing the customer service industry.
Computer assisted learning
Because RAG can reference real-time data input by the user, it can act as an excellent assistant or teacher.
This could mean an AI assistant language teacher that can see where you’re going wrong as you make mistakes and alter its approach to assisting you based on your personal tendencies.
This is already transforming the way we learn, giving more and more people access to expertise that would otherwise have been out of reach, and empowering them to learn at their own pace with guidance tailored to them.
