From writing birthday invites, organising staff schedules, to assisting with complex tasks like writing code, and expert language translations, large language models (LLMs) and the applications that use them have grown vastly in popularity.
It’s unsurprising given their ability to answer questions on a broad range of topics and generate content at speed. However, it’s becoming clear that the most valuable models to enterprises are those that can provide accurate, domain-specific expertise. And this is where many general-purpose LLMs can fall short, held back by outdated or incorrect data, which can lead to inaccurate responses.
In most cases, the solution involves using industry or company-specific data – something most organisations will be wary of plugging into a model. Retrieval Augmented Generation (RAG) is one approach to solving these challenges and seizing opportunities.
Upgrading accuracy and context of LLMs with RAG
RAG is a method that enhances the precision, relevance, and timeliness of large language models by integrating an LLM with a retrieval mechanism that can access data from an external authoritative source outside of the original training, such as company data, or employee records. The retrieval component searches for pertinent data in this secondary knowledge repository, such as a database, and passes this information to the LLM, enabling it to generate more accurate and contextually relevant responses. This process ensures that the output is informed by the most current data for the task at hand.
It is this information retrieval component that 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. And a variety of industries, especially those handling sensitive or specialised data, can begin to maximise the full potential of data-driven LLMs with RAG in their corner.
One of the most important benefits of RAG is the ability to make large language models more agile. Training data can quickly go out of date, but RAG allows volatile and time-sensitive data to be used in an LLM, such as current events. Similarly, RAG architecture allows an LLM to be updated at the point of the user’s request, rather than requiring it to be entirely retrained with new data regularly.
Another key advantage of RAG is its ability to supplement the model with sensitive data that cannot, nor should it have been, included in the initial training of the LLM. RAG is particularly useful for any generative AI applications that work within highly domain-specific contexts, healthcare, financial services and science and engineering for example. Data in these domains tends to be sensitive, and there are various frameworks and regulations in place to safeguard its privacy, meaning training data is often sparse. In turn, RAG is essential to building useful generative AI applications in these industries.
For example, consider healthcare. Patient records and medical history are highly sensitive, and subject to strict 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 and relevant responses to patient queries, enhancing personalised care and decision-making while maintaining data privacy and security.
RAG is not a silver bullet approach
The effectiveness of RAG frameworks depends on the quality of the retrieval system and the data being used. The retrieval database must contain accurate, up-to-date, and high-quality documents to ensure responses are useful and accurate. And while RAG systems are a powerful addition to an LLM’s accuracy, this approach does not entirely eliminate the risks of AI hallucinations, or inaccurate responses.
Similarly, it’s worth noting that RAG systems do not have access to information from the internet in real-time. Instead, these frameworks require pre-indexed datasets or specific databases that must be regularly updated as that data evolves. However, it is usually still much easier to update this additional database than to retrain the foundational LLM.
What’s next for RAG and LLMs?
Given the use cases of RAG, we’re likely to see further research into hybrid models that combine retrieval and generation in AI and NLP (natural language processing). This could inspire innovations in model architectures leading to the development of generative AI capable of taking actions based on contextual information and user prompts, known as agentic applications.
RAG agentic applications have the potential to deliver personalised experiences, such as negotiating and booking the best deals for a vacation. The coming years will likely see advancements in allowing RAG models to handle more complex queries and understand subtle nuances in the data they retrieve.
About the Author
Shane McAllister is Lead Developer Advocacy (Global) at MongoDB. Headquartered in New York, MongoDB’s mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. Built by developers, for developers, our developer data platform is a database with an integrated set of related services that allow development teams to address the growing requirements for today’s wide variety of modern applications, all in a unified and consistent user experience. MongoDB has tens of thousands of customers in over 100 countries. The MongoDB database platform has been downloaded hundreds of millions of times since 2007, and there have been millions of builders trained through MongoDB University courses.
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