What is RAG? An enterprise guide to retrieval-augmented generation

It’s easy to think Large Language Models (LLMs) know everything.

From naming the winner of the 1999 World Series to explaining mathematical concepts like Fast Fourier Transforms, LLMs seem to have an answer for everything. These AI systems, often compared to the human brain for their human-like reasoning and responses, have become the go-to for answering our everyday questions. The numbers themselves are staggering: OpenAI’s CEO Sam Altman reported that ChatGPT alone has over 260 million users a month – more than the entire population of Brazil. 

Yet, similar to the human brain, LLMs have their own limitations – they are prone to “hallucinations,” producing false information, or struggling when given questions beyond their training data. These shortcomings have sparked serious concerns about the reliability of AI-generated content in an increasingly LLM-dependent world. 

Enter Retrieval-Augmented Generation, or RAG: an information retrieval technique introduced by former Facebook AI researcher Patrick Lewis and his team in 2020. RAG aims to address these challenges by reducing hallucinations and expanding the knowledge base of AI models, improving the trustworthiness of the AI systems we increasingly rely on.

What is RAG in AI? 

Retrieval-Augmented Generation (RAG) is an AI technique that enhances the abilities of Large Language Models by accessing outside knowledge sources. RAG allows LLMs to look for additional information in real-time before generating their responses, resulting in outputs that are more relevant, accurate, and timely. 

LLMs, such as OpenAI’s ChatGPT, Meta AI’s Llama, or Anthropic’s Claude, are trained on vast amounts of data to comprehend and generate human-like language. To put this into perspective, ChatGPT 3.5 was reportedly trained on approximately 570GB of text – the equivalent of reading a million books! The training process is no small feat either, being computationally intensive and time-consuming and often requiring powerful GPUs, costing millions of dollars, and taking several months.

Once trained, an LLM can generate responses based on the knowledge it has “learned.” However, this knowledge is only a static snapshot of the world at the time of training. LLMs themselves cannot learn new information or update their “snapshot” without being retrained. The information the LLM used to generate responses is limited to its trained information, which can be months out of date or not include specific information.

This is the exact benefit of RAG: allowing LLMs to access current or specialized information outside their own knowledge base without needing to completely retrain.

RAG is particularly valuable for businesses deploying LLMs in specialized contexts. What if you want your conversational AI to talk about your new products, roleplay as your customers for sales training, or abide by your internal guidelines? Much of that information is proprietary and not part of the LLMs training data, making RAG a valuable tool for businesses looking to augment their LLMs with company-specific knowledge bases.

How does RAG work? 

Let’s imagine you are a pharmaceutical company, and you want to create an experience that answers user’s questions about specific medications. A traditional LLM alone falls short here. It would not have enough specific information to answer your user’s queries and is prone to hallucinations - responses with false information - which can lead to inaccurate and potentially harmful answers. RAG, on the other hand, would allow you to create an experience with real-time, specific information. 

To start off, RAG requires a knowledge base of relevant information to pull from. This could be dosage information, clinical trial results, pharmaceutical manuals, or information from proprietary databases. When a patient asks a question about proper usage or a doctor asks about drug interactions, we want to be able to find the relevant information in the knowledge base quickly and accurately. We can do this by transforming the knowledge base into a vector database. 

"This is the exact benefit of RAG: allowing LLMs to access current or specialized information outside their own knowledge base without needing to completely retrain."

Vector databases allow us to search for information matching the meaning of a question, rather than keywords. For example, let’s imagine a user searches “What are the side effects of aspirin?” A keyword search would only look for documents with the exact words “side effects” and “aspirin.” Though this is helpful, it could miss documents with important information that do not directly use those keywords. On the other hand, semantic search tries to understand the meaning of the question and search for relevant information. It would search the database and return documents such as: long-term effects of painkiller use, complications of blood-thinning medications, symptoms of aspirin sensitivity or allergy. By understanding the intent of the question, we get more comprehensive answers that keyword searches could miss. 

To create a vector database, we transform all the documents into the knowledge base into embeddings, a list of numbers that captures meaning. When a user asks a question, such as “What are the side effects of aspirin?”, the question is also transformed into an embedding. RAG then searches through the database for the document embeddings that are most similar to the questions embedding, returning the most relevant information. Once we’ve found the knowledge we need, the information and the original question are given to the LLM, which uses it to generate a specialized response. 

With RAG, our pharmaceutical conversational AI can provide correct, up-to-date information about medications, their side effects, interactions, and more, based on specific information from its knowledge base. 

Unlike updating an LLM, which can be expensive and time consuming, RAG databases can be continuously updated with new data at a low cost.

What are the benefits of RAG?

RAG not only allows you to tailor your LLM to a specific context but also offers several advantages over using LLMs alone.

Unlike updating an LLM, which can be expensive and time consuming, RAG databases can be continuously updated with new data at a low cost. This allows your system to be linked to massive amounts of data available both inside and outside your organization in real-time. Any updates to technical and policy manuals, training videos, sales playbooks, customer relationship management tools, and historic sales info, as well as external market databases and indices, can be integrated into your LLM responses immediately.

Additionally, vector databases can provide specific sources of information, unlike LLMs alone. This capability allows you to cite sources when generating its output, enhancing the transparency of your responses and increasing trustworthiness. In fields requiring high accuracy, such as finance and healthcare, transparency of source information is a large benefit. With RAG, you also have greater control over the information the system uses to generate responses, which can help prevent potential compliance risks. 

Lastly, LLM famously struggles to answer questions without having enough information – often causing hallucinations. AI hallucinations have already landed some professionals in hot water, including lawyers, researchers and journalists.  Enabling LLMs to reference more context-specific information in real-time through RAG means the model isn't constrained by its training data. When there aren’t strong public sources of information for the LLM to draw from, it won’t so readily resort to generating false information, using RAG instead to check other sources.

The improvements provided by Retrieval-Augmented Generation (RAG) are critical in industries where accuracy and relevancy are paramount, which is why so many organizations – including our clients – are taking this technology seriously. At UneeQ, we've integrated RAG into our proprietary orchestration tool, Synapse™, to create AI-driven digital humans that truly understand your products, and can communicate with your customers in their own language.

Synapse, much like the human brain, brings together multiple aspects of human-like conversation: context awareness, memory retrieval, and adaptive response generation. By leveraging RAG technology, Synapse can access vast repositories of specialized knowledge in real-time, ensuring that our digital humans' responses are not only conversationally fluent but also grounded in accurate, up-to-date information.

Can digital humans integrate with RAG?

UneeQ harnesses the power of RAG to seamlessly integrate internal and external knowledge bases. This capability is a cornerstone of our Synapse platform, which helps us transform digital humans into knowledgeable product experts and authentic brand ambassadors.

There are many advantages of deploying digital humans with access to the most current data within your organization:

1. Enhancing customer experience

‍For many customers, navigating websites or reading through product information can be daunting and frustrating. At UneeQ, our digital humans distill complex technical details into personalized conversations, allowing customers to talk in their language while still connecting with your products. One eCommerce assistant, for example, can match customers to the right product using extensive knowledge in real time, like recommending the ideal camera for an Indonesian sightseeing trip. This approach can help increase conversions, increase customers confidence with purchasing decisions, and enable more upselling opportunities. 

2. Event concierge

‍In 2024 alone we’ve seen many of our clients using their digital humans to provide extra interactivity and engagement at the in-person events and conferences they attend. Qatar Airways, for instance, employed their UneeQ digital human, Sama, to speak with attendees at Farnborough International Airshow, where she drew upon her knowledge of the airline’s brand and travel destination information to give more personalized conversational experiences to customers interested in booking their next vacation. She also gave an impressive address to journalists alongside Qatar Airways’ Group Chief Executive Officer to help launch their next-generation Q Suite business class.

‍3. Advanced sales training

‍Our digital humans can help you train your sales staff by engaging in realistic role-play scenarios. Powered by RAG, these digital trainers can tap into your sales playbooks, brand voice guidelines, and detailed customer personas, to deliver rich, nuanced conversations. We’ve seen this enhance training effectiveness and ensure your sales team deeply understands your product or services. By learning through naturally interacting with a digital human, UneeQ can help you produce more confident, knowledgeable sales representatives to serve your customers.

From RAG to riches? How to start using RAG

If you’re now familiar with RAG and would like to speak to one of our experts, we’re here to show you how it works in practice and to help you get set up.

You can book a meeting here. Our team will be happy to discuss the UneeQ platform and to get your RAG journey started.