Learn what RAG is, how it works, and how this approach can make genAI more accurate, customizable, secure, and affordable for businesses of all kinds.
Since ChatGPT’s launch in late 2022, business leaders have rushed to put generative AI (genAI) to use in their organizations. In one recent study, for instance, almost 50% of all CEOs revealed that their organizations are already experimenting with AI solutions, while industry leaders like Amazon, Meta, JPMorgan Chase, and Boeing have developed their own to help with product recommendation, fraud detection, and even air-traffic management.
But these unique technologies also come with unique problems, such as those stemming from large language model (LLM) accuracy, bias, and data security. Thankfully, a new solution promises to (in part) solve these issues: retrieval-augmented generation, or “RAG.”
Let’s dive into what RAG is and how it works, then think through how this approach can make genAI more accurate, customizable, secure, and affordable for businesses of all kinds.
What is retrieval-augmented generation (RAG)?
In general, AI tools such as ChatGPT run on LLMs. These models are trained on enormous amounts of (typically open-source) data, which enables them to identify patterns when issuing responses to a user query.
However, most LLMs have no inherent mechanism for verifying the quality or veracity of their underlying training data. This, coupled with the fact that most LLMs are trained on public datasets from the internet, leaves users open to unexpected risks. For instance, a number of popular genAI tools have been guilty of spreading disinformation and entrenching bias.
These risks are even greater in the realm of business. In addition to producing inaccurate or biased information, we now know that chatbots regularly infringe on IP. Also concerning is the fact that many businesses try to optimize genAI outputs by providing models with internal data of their own, which can lead to the unintentional disclosure of proprietary, confidential, or personally identifiable information.
The RAG approach optimizes AI output by allowing LLMs to retrieve information that is external to the datasets or knowledge sources upon which they’ve been trained. What this means in practice is that RAG-powered LLMs generate more accurate and contextually relevant information for their users—all while sidestepping the need to rebuild the LLM in question.
How can RAG benefit businesses?
Without removing the necessity for enterprises to monitor employees’ use of AI, RAG can boost operational efficiency in four key ways.
RAG makes enterprise genAI tools more customizable.
Developers, for example, can use it to fine-tune an AI model’s data sources with an eye on their organization’s needs. RAG systems also grant businesses more granular control over genAI responses to user queries, which enables developers to test and improve the quality of these tools with greater speed.
RAG improves the accuracy of enterprise AI solutions.
By making it possible for AI chatbots to draw on external knowledge sources in real time (be they trusted news sites or social media, internal reports, or up-to-date financial and customer information), RAG improves LLMs that would otherwise be restricted to static and potentially inaccurate sources.
RAG also bolsters genAI security.
This thanks to the fact that many RAG-powered AI services (such as those offered by Oracle) allow companies to maintain complete control and ownership over any data used to supplement an LLM. Many such services also promise to not mix customer data, which, in addition to preventing unintended breaches, permits organizations to shore up consumer trust.
RAG makes high-quality AI more affordable.
Previously, if a business wanted to customize a genAI tool, their developers would need to completely retrain its underlying model. Now, RAG provides a cost-effective key for unlocking AI solutions that are at once customized, accurate, and secure.
Where can organizations put RAG to use?
Now that we know how RAG can benefit businesses, let’s explore exactly where an organization might put it to use for human capital management (HCM), enterprise resource planning (ERP), and customer relationship management (CRM).
AI for HCM
Imagine that a medical equipment manufacturer wants to use a genAI solution to streamline hiring and onboarding. Rather than risk compromising applicant security with an open-source chatbot, they could develop a RAG-powered LLM with which to analyze candidate profiles, identify those best suited to their needs, and even suggest interview questions. From there, the company might use a RAG AI onboarding assistant to answer questions for new hires, grant them system permissions (e.g., access to confidential business information), and walk them through training modules—thus saving HR teams considerable time.
AI for ERP
This same organization could also use a RAG model to streamline resource planning. When budgeting and purchasing supplies, for instance, the company could turn to a customized AI assistant (with confidential access to internal operations data) to compare vendor prices and offerings, track resource usage, and automate order management. Doing so would enable them to speed up time to market for new products and enhance their quote-to-cash process.
AI for CRM
This medical device company could also use AI to provide a seamless customer experience. By analyzing customer profiles and order histories, for example, the right AI tool could automate customized offer emails. If given the ability to reference real-time accounts data, existing contracts, and service portfolios, AI solutions could generate detailed sales proposals and decks. In addition to meeting demand with supply, this would enable sales teams to be more efficient and effective.
By taking an RAG approach, organizations like this can take full advantage of genAI’s benefits, all while resting assured that their data will remain secure, their employees informed, and their customers loyal.
