RAG to the Rescue

Large language models (LLMs) have captured the public imagination with their ability to generate human-like responses. But the ability to create sonnets and write code within seconds will rarely deliver tangible value or ROI for businesses. Instead, it’s the accuracy, specificity and domain expertise that make AI tools useful.

Retrieval augmented generation (RAG) is the key to providing this missing layer of detail. More importantly, it’s unlocking new possibilities for generative AI applications in industries that have so far been unable or unwilling to implement this technology.

How Does RAG Work?

RAG works by taking a query and searching a knowledge base – a database in most cases – to retrieve relevant results in combination with a foundational model, like GPT4, to generate a response that is coherent and factually grounded. This separate source of knowledge is usually information that is too sensitive to be fed into the initial training of the LLM but is crucial in enhancing the accuracy and relevance of the response. For example, employee records can highlight how much holiday a specific employee has left instead of simply citing the general figure from the company’s policy handbook.

This information retrieval component is at the heart of how RAG works, and how it's differentiated from general LLMs. Chatbots and other technologies that use natural language processing can massively benefit from RAG.

Related:Overcoming the Perceived AI Skills Gap

RAG in Action

Various industries, especially those handling sensitive or specialized data, can begin to maximize the full potential of data-driven LLMs with RAG in their corner. In highly domain-specific contexts, like healthcare, financial services, science and engineering, data is subject to various frameworks and regulations to keep it safe. This private and sensitive training data is not, and cannot be, exposed to LLMs to train on. But with RAG, we can have the best of both worlds.

As an example, consider patient records and medical histories. These contain sensitive information protected by privacy laws. While such records would never be included in the initial LLM training, RAG can integrate this data during runtime, allowing a healthcare professional to make queries about patients without compromising their data. This enables RAG applications to offer more precise, up-to-date and relevant responses to patient queries, enhancing personalized care and decision-making while maintaining data privacy and security.

All of that said, it's important to remember that RAG is not a silver bullet. The quality of the information in the database still determines the quality of the output. So if the data is inaccurate, outdated or incomplete, the AI's responses will be unreliable. As a result, RAG requires pre-indexed datasets or specific databases to be updated as that data evolves.

Related:Combating Generative AI’s Hallucination Problem

Towards Nuance and Complexity

Ultimately, RAG technologies are leveling up the capabilities of standard LLMs. By combining the power of LLMs with advanced information retrieval, we’re seeing a new wave of generative AI applications emerge. These are smarter, more accurate, and much more adaptable. This breakthrough is not only driving innovation but also inspiring a new wave of AI research, promising even more sophisticated models capable of handling complex queries with nuance.