The Power of RAG
Think back a few years to OpenAI’s initial introduction of its generative AI (GenAI) chatbot—the excitement, the promise of marvelous opportunities, the sense of a change for the better. It wasn’t long before the euphoria of GenAI wore off and people started noticing that large language models (LLMs) could produce absolutely fabulous results, but they could also deliver flawed information that looked plausible but was untrue. That’s when the reality of hallucinated data began to hit home.
Librarians were among the first to call the validity of AI-generated information into question. Suddenly they were confronted with requests from their users for articles that didn’t exist. The citations were properly formatted and looked real, but they weren’t. Often, even the journal was nonexistent. GenAI delivered citations that LLMs created based on their training sets rather than established databases that accessed actual documents. Recognition of the limitations of AI-generated data quickly spread to the legal community, as several attorneys were caught using spurious, GenAI-hallucinated information in court filings and other legal documents. One hopes no surgeons relied on GenAI to guide their scalpels.
The notion of hallucinations—how to detect and guard against them—quickly became part of the discourse about the utility of GenAI. For those doing casual searches using GenAI chatbots, some of the hallucinations were actually very funny and obviously false. A query about upcoming marathons in the Northeast U.S. that included a reference to one in California showed a basic misunderstanding of geography.
A list of high protein foods? Nope, not cucumbers, but that green vegetable somehow snuck in. As technological developments around GenAI arrived at a dizzying pace, the need to ensure trustworthiness became a major concern within enterprises. Hallucinated information has serious ramifications for companies well beyond geographic and nutritional missteps. Get a manufacturing spec wrong, fabricate a policy statement, or misinform about pricing, and the company has a huge problem with very serious consequences.
RAG POWERS DOWN HALLUCINATIONS
Enter the power of retrieval-augment generation (RAG). Particularly within the enterprise, the necessity of having trustworthy, hallucination-free information is paramount.
RAG holds the promise of vetting information from LLMs against external data for validation, grounding GenAI responses in real-time, authoritative data sources. External, for some deployments, could mean a validation of data by going outside the organization to reputable sources on the open web, often to overcome date limitations of the LLM’s training data. If the training data was current only as of 2024, and it’s now 2025, that data could be out-of-date. External, for general-purpose LLMs, could mean Wikipedia or another free, web-based data source.
Within organizations, which is more likely the case for enterprise AI, external means reaching out to documents held within the organization but not part of the LLM’s training set. As with more general cases, this could be to ensure the most recent data is returned to queries, but it could also validate that the response is confirmed by additional data as true and reliable and does not merely sound plausible. It could reach out to knowledge hubs containing information proprietary to the enterprise, such as pricing, policies, customer conversations, memos, drawings, videos, and a host of other internal sources that would be very familiar to knowledge managers.
In essence, RAG is a mechanism that enhances an LLM’s ability to deliver trusted information, making search results diamond tough yet rich as cream. Although that sounds simple, the reality of getting it to work as anticipated is more complicated. With RAG, a user’s query is routed both to a set of curated information and to an LLM. This combination helps ensure that the training data in the LLM is validated against relevant and authoritative data from internal databases, documents, and other sources chosen by developers as appropriate. Depending on the query, RAG could go outside the internal information, possibly even to Wikipedia and its ilk.
Beyond reducing hallucinations and enabling access to the most current data available, RAG provides source attribution. This is particularly helpful if the user needs to know more details about a topic and can go directly to the source to satisfy that need. This also adds to the trust factor. RAG models can be customized to the domain specified by an organization.