How Retrieve-Augment-Generate (RAG) improves generative AI models

This article was made possible thanks to the generosity of our sponsor, Zetaris, a data management platform founded in 2013 that aims to make data analysis easier, more accessible, and faster for businesses, empowering them to gain valuable insights and remain competitive in the evolving market.

There’s a new acronym in the tech space – RAG.

Generative AI has quickly transitioned from being a novelty and is now an essential business tool. Built on large language models (LLMs) that have ingested vast quantities of data, generative AI can answer questions and solve problems faster than ever before. However generative AI tools are also prone to errors and hallucinations where the data in the LLM is either incorrect or lacks context. This is why RAG, or Retrieve-Augment-Generate, is so important.

Like any information system, generative AI is subject to the garbage in, garbage out principle. The creation of an LLM depends on each step in RAG to deliver the best possible inputs so the risk of an incorrect result is reduced. While LLMs have a lot of general knowledge from pre-training, they need mechanisms to quickly retrieve and leverage external data so they can produce more up-to-date, complete and accurate outputs. Businesses using generative AI need the most recent data to ensure they receive the most accurate answers to their queries.

For an LLM to provide the most accurate outputs, generative AI needs to retrieve relevant information from external sources like reports, websites, databases or knowledge bases quickly. The newly retrieved information needs to be augmented so the language model understands the information’s context. Armed with new information and its context, the model can then generate a final output like an answer, analysis, or text.

RAG enhances generative AI systems by enabling the integration of external data sources to augment the pre-trained model’s knowledge in a systematic way during inference time. This hybrid approach improves performance on many tasks.

With the number of generative AI user cases proliferating, RAG delivers better outcomes for queries in applications such as chatbots, virtual assistants and natural language analytics agents. Organisations of all sizes across every vertical use a varied landscape of data sources and systems. With RAG, all those data sources can be used to enable faster and more accurate outputs from generative AI tools.

Retrieve-Augment-Generate in Healthcare

Using a healthcare example, questions such as “How many beds are available in Ward F right now, and where are they?” can be answered quickly and accurately using data from patient management systems. Leaders who manage teams with performance goals can ask “What is the average productivity percentage of my team for the week, and how does that compare to our target? Are there any outliers?” And receive an answer using the most recent sales data.

Hospitals seeking to exploit a RAG approach can use tools so they can make predictions that enable healthcare workers to ask, “How many beds will we need in Ward F next week?” given factors such as external events and weather – data that is not available in traditional patient management systems. This can be integrated into the LLMs that power generative AI applications. It works by taking a decentralized approach to data management and using a semantic layer that points systems to data at its source without the need to move or copy data to a centralized data lake or warehouse.

The efficacy and effectiveness of generative AI tools are dependent on the quality of data that are used to train and continually enhance LLMs. RAG ensures that the most current data, wherever it’s stored, is retrieved and augmented with contextual information so the tools can generate the best possible response to make predictions that enable organizations to better plan and manage resources.

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