Retrieval Augmented Generation (RAG): What Is It and How Do Enterprises Benefit?
Retrieval augmented generation, or RAG, augments a large language model’s (or LLM) predictive abilities by grounding the model with information from external knowledge that is current and contextual.
LLMs such as GPT-4 represent a significant advancement in natural language processing abilities, which enables computers to process, understand, and generate human language.
Despite the seemingly human-like capabilities LLMs have brought into our everyday lives, we also know about the limitations and risks. LLMs are prone to hallucinate, provide misinformation, and cannot grow their knowledge outside of their training data. Additionally, they can pose serious security and privacy risks.
This is where RAG comes in.
What Is Retrieval Augmented Generation?
RAG is an AI framework that improves the quality of LLM output by introducing an information retrieval system that draws from trusted sources of knowledge. LLMs on their own are limited to their training data, stuck in the time when their training ended.
Still, their aim is to predict the best next piece of text based on the user prompt and their training — whether it’s factually correct or not. LLMs with RAG, however, have the ability to access updated and contextual knowledge or documents in response to a query. This is called grounding.
By first retrieving only the information that is relevant to a query and a user, RAG can help produce the most up-to-date, accurate response to a prompt. The concept first came into prominence through the 2020 data science research by Patrick Lewis and a team from what was then called Facebook (now Meta).
For an enterprise, RAG provides greater control over the quality and context of data the LLM uses to generate its responses. This could mean restricting a model’s answers to pull only from an enterprise’s approved company procedures, policies, or product information. Using this approach, enterprises can provide the LLM with greater context for queries and ensure greater accuracy.
RAG particularly suits tasks that are knowledge-intensive, meaning tasks that most humans would need to turn to an external source of knowledge to complete.
However, the above-described process is considered naive RAG. RAG as a concept is fairly new, and what this provides is not always enough to satisfy production-grade requirements. This is why Generative Answering combines RAG with AI relevancy (considering the user’s intent, context, and behavior) to achieve an advanced application of RAG.
Advanced RAG techniques take this one step further, by including additional context that further refines a user’s prompt or query. Generative Answering makes use of AI relevancy, a formula we’ve been working on for nearly 20 years.
Why Do Enterprises Need RAG?
Interest in retrieval systems has grown with the need to overcome limitations in LLMs to apply them in real-life scenarios in which accuracy and timeliness are important. We run into the following issues with pre-trained language models:
Difficulty in extending knowledge
Outdated information
Lack of sources
Tendency to hallucinate
Risk of leaking private, sensitive data
RAG attempts to address these challenges when working with language models. Let’s next look at how RAG accomplishes this.
How Does RAG Work?
Typically, a pre-trained language model takes a user prompt — or query — and generates a response based on what the model knows from its training data. The model draws from its parametric memory, which is a representation of information that’s already stored internally in its neural network.
With RAG, a pre-trained model now has access to external knowledge that provides the basis for factual and up-to-date information. The retrieval system first identifies and retrieves from external sources the most relevant pieces of text based on the user’s query.
Techniques such as word embeddings, vector search, and other machine learning models assist in finding the most relevant information for the user’s query in the current user’s context. In an enterprise setting, external sources may be knowledge bases with documents on specific products or procedures or an internal website for employees such as an intranet.
When a user submits a question, RAG builds on the prompt using relevant text chunks from external sources that contain recent knowledge. The “augmented” prompt can also include instructions — or guardrails — for the model. These guardrails might include: don’t make up answers (hallucinate), or, limit responses to only the information found in the approved trusted sources. Adding contextual information to the prompt means the LLM can generate responses to the user that are accurate and relevant.
Next, the model uses the retrieved information to generate the best answer to the user’s query through human-like text. In the generated response, the LLM can provide source citations in order to give the user the ability to verify and check for accuracy, because the LLM is “grounded” with the identifiable retrieved information from the retrieval system.
Applications of Retrieval Augmented Generation
RAG has the potential to greatly enhance the quality and usability of LLM technologies in the enterprise space. Some of the ways businesses can use RAG include:
Search: By combining search with a retrieval-based LLM, your search index first retrieves documents that are relevant to your query before responding to the user. The generative model with this approach can provide a high-quality, up-to-date response with citations. This should also significantly reduce instances of hallucinations.
Chatbots: Incorporating RAG with chatbots can lead to richer, context-aware conversations that engage customers and employees while satisfying their queries.
Content generation: RAG can help businesses in creating content in areas such as marketing and human resources that are accurate and helpful to target audiences. Writers can gain assistance in retrieving the most relevant documents, research, and reports.