What is Retrieval Augmented Generation (RAG)?

Retrieval Augmented Generation (RAG) is one of the most valuable techniques driving effective Large Language Model (LLM) implementation in today’s business environment. In this blog post, we explore how RAG helps tailor LLMs to specific applications, organizations, and business areas, ensuring models can provide technical, insightful, and accurate responses to specialized inputs. At the same time, it makes AI LLM technology more accessible and affordable for those organizations that cannot train their own models.

What is RAG (Retrieval Augmented Generation)?

Retrieval Augmented Generation (RAG) definition: RAG is an AI technique or framework that optimizes LLM responses by directing the model to reference specific data sources outside its original training data. It combines the general natural language capabilities of extensively trained LLMs with more nuanced and extensive insights into highly specialized subject areas. This makes RAG a useful tool for tailoring LLM performance to particular business applications and ensuring it can engage with domain-specific terminology, leverage up-to-date information, and provide valuable outputs.

Why use Retrieval Augmented Generation?

RAG techniques compensate for the weaknesses of LLMs when implemented in more niche applications. In these instances, LLMs traditionally struggle due a lack of context-specific information.

While LLMs are trained on vast amounts of data, this may not include sources relevant to your application. For instance, the LLM powering your customer support Chatbot may not have been trained on the troubleshooting guidance for your key products. Consequently, it won’t have the detailed information required to provide accurate or helpful responses. Compounding this, LLMs also have a training data cut-off point, which limits their ability to provide up-to-date information based on the latest sources.

Hallucinations are also an issue. When LLMs don’t have an accurate response to an input, they can “hallucinate.” This involves the LLM generating incorrect responses to inputs. Users typically have no way of distinguishing these “hallucinations” from accurate responses and may be misled as a result. Similarly, LLMs that do not have the data required for an in-depth, accurate answer will often generate a vague, generalized response that, at best, provides little assistance to users or, at worst, misleads them.

LLMs require incredible amounts of training data. This makes them necessarily broad in scope. Retrieval Augmented Generation techniques compensate for the deficiencies of general models by introducing domain-specific data sources and narrowing their focus.

What are the alternatives to RAG?

Retrieval Augmented Generation is just one approach for improving LLM performance and the accuracy of responses. LLMs can also be honed and refined with the following alternative techniques:

Prompt engineering

Prompt engineering involves carefully designing prompts to improve LLM outputs without modifying the model. It means creating specific instructions and examples and may involve structured prompts, few-shot learning (a machine learning framework in which an AI model learns to make accurate predictions by training on a very small number of labeled examples), or prompt chains. Prompt engineering is a fast and cost-effective method for improving response accuracy and is best applied when additional data sources are not required and the task is within the LLM’s general capabilities.

Fine-tuning

Fine-tuning is similar to Retrieval Augmented Generation in that it involves updating a pre-trained LLM on smaller, more specialist datasets, exposing it to specialist knowledge, styles, and behaviors not covered by the original training. It has higher up-front costs than prompt engineering but is often a more efficient solution when dealing with long, complex, and unwieldy prompts. In contrast with RAG, it does not allow for access to dynamic or external data. Instead, its responses are determined by the static, established knowledge in its (re)training data.

Pretraining

Pretraining is the process of building an LLM from scratch. It requires enormous datasets consisting of billions or even trillions of tokens and is expensive and resource-intensive. Although it is not a practical solution for most business applications, it delivers maximum control and enables creators to tailor the model to their precise needs.

As such, Retrieval Augmented Generation is typically the most appropriate approach when you want your LLM to engage with dynamic datasets and external information, enabling it to leverage up-to-date information and improve output accuracy.

Business challenges and how RAG helps

Considering specific business challenges enables us to better understand the value of Retrieval Augmented Generation and the strengths of the approach. One of the principal challenges organizations face when attempting to leverage AI technology is that LLMs don’t know you or your data. The sheer amount of information required to build an LLM means they are usually trained on public data. While this data is diverse and varied, it does not include all the privately-held information stored by most organizations. Crucially, it will not have been trained on your business’s data. Nor will it have access to data generated after their training cut-off. This means it is stuck within a limited frame of reference.

The general, broad-perspective approach to LLM training is limiting in other ways. While public-facing LLMs like ChatGPT benefit from being do-it-all, general tools, that’s not what we want from powerful business technologies designed to meet specific commercial needs. For instance, an LLM-powered employee assistant that helps individuals with questions concerning employee benefits must have access to your HR policies and documents to provide meaningful responses. If not, it will generate inaccurate outputs based on general HR policies and information.

Benefits of Retrieval Augmented Generation

First and foremost, Retrieval Augmented Generation improves response accuracy. By enabling the LLM to reference external datasets that provide additional information and context, RAG reduces the likelihood of hallucinations and improves the model’s ability to generate insightful and valuable responses. At the same time, it overcomes the limitations of time-limited training and enables LLMs to reference current and new information. This results in models that remain relevant and are capable of working with the latest available data.

The ability to change data sets and information sources quickly and easily using RAG also gives developers more control and a means to adapt and improve models efficiently. This means organizations can adapt to evolving demands and new operational requirements with greater agility. Likewise, developers can dictate authorization permissions, allowing the LLM to tailor responses to individual users and their respective access levels.

Finally, commercial use of LLMs is usually based on Foundation Models (FMs) like OpenAI’s GPT series, Google’s PaLM, or Meta’s Llama. The cost of retraining these FMs for use by single businesses is prohibitively high. A major benefit of RAG is its ability to enable organizations to tailor these FMs to their needs, making LLM technology more accessible and affordable.

Retrieval Augmented Generation business use cases

Businesses use RAG to prepare LLMs for use in several applications. Here are several of the most common use case for Retrieval Augmented Generation:

Customer support chatbots

LLM-powered customer support chatbots benefit from RAG, which enables them to generate more accurate responses from company documents, product information, and knowledge resources. This results in greater customer satisfaction, improved first-contact resolution rates, and lower customer support costs.

Search augmentation

Incorporating LLMs that leverage Retrieval Augmented Generation into search functions enables engines to supplement results with AI-generated natural language responses that streamline processes and make it easier to find information. For instance, if a user searches a particular question, the LLM can provide an answer that prevents the user from clicking through various links, scanning pages for relevant information, and then extracting the data from the website. In this example of Rag, the technology improves the chances of the AI-generated response meeting the user’s needs.

Knowledge resource

RAG can also play a significant role in creating effective knowledge resources. These enable employees to query company documents and data, help them make informed decisions, and ensure organizations maximize the value of their stored data. For instance, employees could ask the knowledge resource to extract information relating to specific regulatory requirements, streamlining the processes of demonstrating financial compliance.

Text summarization

Text summarization and consolidation is another business use case for RAG. Organizations could use LLMs to extract critical insights and information from dense, text-heavy reports and filings. Here, RAG ensures that the LLM can understand and engage with domain-specific terminology and know what information is most relevant and valuable to the reader.

Personalized recommendations

Personalized user or customer recommendations depend on an up-to-date understanding of the individual’s behavior and preferences. They should be based on the user’s most recent purchases and reviews.. . In this Retrieval Augmented Generation use case, LLMs analyze up-to-date data that wasn’t included in its original training but now sits in an organization’s CRM or e-commerce platform.

Business intelligence

The final business RAG use case is for intelligence data analysis and BI. AI is increasingly critical in providing business intelligence and informing strategic decision-making. LLMs’ abilities to analyze market movements, interpret large amounts of data and identify trends mean that they are regularly used to inform business planning. Retrieval Augmented Generation ensures that those LLMs are hyper-focused and equipped to interpret information accurately in niche markets and subject areas, making them more valuable to businesses operating in those sectors.