How RAG Is Helping to Realize the Promise of LLMs

It has been almost two years since OpenAI opened ChatGPT to the public. In that time the technology has upended multiple industries, rewired the working practices of millions and generated thousands of think-pieces and podcasts about what AI has in store for us.

And yet despite all this activity, several fundamental concerns are still unresolved. People are excited by LLMs. They are using LLMs in huge numbers. But they still—two years in—do not fully trust them.

The reasons for this are manifold, but the major problem has remained the same from day one: hallucinations. LLMs are still frequently spitting out answers that are inaccurate and illogical. Organizations want to use LLMs to interface with customers. They want to use them to make high-stakes business decisions, but the persistent threat of hallucinations makes fully trusting LLMs difficult.

To this point, the primary method of combating these hallucinations has been fine-tuning, i.e., tweaking a model that has already been trained, to specialize its capabilities. However, there are significant problems with this approach. For instance, fine-tuning requires extremely advanced technological skills, which many organizations lack.

Recently, a more effective solution has emerged—and organizations are increasingly turning to it to refine their LLMs and reduce hallucinations. It’s called Retrieval Augmented Generation, or RAG.

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What Is Retrieval Augmented Generation (RAG)?

One way to think of RAG is as a kind of high-tech fact-checker. It grounds AI responses in a structured knowledge graph and validates them against a comprehensive knowledge model. In turn, it significantly reduces hallucinations and enhances accuracy. By linking business data to generative AI models, RAG adds specific context and meaning. This can help the AI understand the data while creating a strong framework for accurate response generation.

As mentioned, this has notable implications for hallucinations, which can be greatly reduced through the strategic application of RAG. But what it really does is create trust.

Among the most common complaints about LLMs is that their outputs lack transparency. A given answer might seem correct, but—as a schoolteacher might put it—the system fails to show its work. To have full confidence in the outputs of their LLMs, organizations need to know how they arrived at their answers. RAG makes this kind of verification possible, allowing organizations to track how LLMs got from point A to point B.

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RAG vs. Fine-Tuning: Faster, Cheaper, More Secure

These LLMs are being used in fast-paced business environments, where data is continually evolving. Here, too, RAG has an essential role to play.

RAG’s framework is, by design, flexible and model-agnostic. This means that businesses can quickly upgrade their models while avoiding the lengthy downtimes endemic to conventional fine-tuning processes. Organizations can—with relatively minimal effort—enhance the domain-specific knowledge of their LLMs, infuse them with fresh documents, plug them into relevant online sources and more. With this method, the LLMs becomes a dynamic entity, expanding alongside the organization and adapting to its needs.

Think of it this way: fine-tuning is like putting the LLM through school, laboriously teaching it what it needs to know for months. RAG is more like a brain implant—porting in the relevant information and the context to properly understand it.

Of course, given the current cyberthreat environment, many of those needs will be tied to security protocols—and here too RAG has distinct advantages. Unlike fine-tuning, RAG permits organizations to apply real-time data security. They can decide in advance which document can be seen by which audience. They can get even more granular and decide which field of which document should be seen by which audience. This enhanced flexibility is essential, especially because some LLM outputs are consumer-facing.

Crucially, all of this can be accomplished without breaking the bank. RAG optimizes the use of processing power, leveraging the rich contextual data offered by the knowledge graph to permit smaller, more focused prompts.

LLMs will never reach their full potential without organizational buy-in—and they will never get that buy-in if they can’t be trusted to work. RAG makes LLM adoption a much simpler and less stressful process. Newly fortified by up-to-the-minute data and robust contextual insights, these RAG-enhanced LLMs help to realize the vision first posited two years ago: a world in which LLM outputs are accurate, transparent and hallucination-free.

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