What is Retrieval Augmented Generation? The benefits of implementing RAG in using LLMs

Retrieval Augmented Generation, aka RAG, is a technique for augmenting LLMs’ capabilities via relevant documents that are separate from the LLM itself. Think a kitchen sink of all the text data in your organization—your emails, Slack channels, Notion documents, policies, etc.—made available to improve an LLM’s performance.

If you’re familiar with Tonic (6-year-old generative AI player in the data synthesis space), you might be asking yourself why we’re talking about RAG right now. In the spirit of safely unlocking an organizations’ data to maximize its use in software development and testing, we’ve been looking into how teams can safely implement LLMs to answer targeted questions using the multitudes of data organizations produce every day. In other words, we’ve been having some fun playing around with RAG. 

We’ve found RAG to be the cheat code to leveraging LLMs to get responses specific to your organization by best linking your prompts to the data they need. In this article, we’ll share everything we’ve learned about this innovative new method, from how it works to how it differs from other methods like fine-tuning, as well as the benefits of RAG.

Our goal? Helping you leverage RAG to speedrun your LLM development.

What Is RAG and how does RAG work?

Retrieval-Augmented Generation (RAG) enhances the functionality of LLMs by integrating real-time data retrieval into the model's generative process. Unlike traditional LLMs, which rely solely on pre-trained models, RAG dynamically pulls information from external knowledge sources (such as the internet or a proprietary database) into its responses.

RAG was introduced in the paper, Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks in 2020 and has exploded in popularity with the acceleration of LLM capabilities. The method uses an LLM as part of a larger system that is integrated with a document store, but does not change the LLM.

RAG works behind the scenes of your LLM queries. Given a user’s search query, RAG consists of the following steps:

1. The documents with relevant contextual information for answering the user’s prompt are retrieved from the document store (steps 3 and 4 in the above diagram).
2. The user’s prompt is augmented with the relevant contextual information and sent to the LLM (step 5 in the above diagram).
3. The LLM generates a response that is sent back to the user (steps 6 and 7 in the above diagram),

The retrieval process is what makes RAG unique. In the above diagram, the documents repository represents the vector database that stores the semantic information from the documents in the document store. Each document in the document store is chunked into pieces and converted to vectors that store the semantic meaning of each chunk. The vectors as well as the metadata for each chunk are stored in this vector database. An algorithm is used to take the user query and determine the relevant text chunks to retrieve from the vector database to answer the user’s query. From there, a variety of different methods are used to augment the query to construct a robust prompt to pass to the LLM. 

The generated response you get from the LLM is more precise than from a purely generative model, as it draws on your specific data rather than just the basic knowledge used to train a foundation model. 

5 key benefits of RAG

Implementing RAG enriches LLMs with real-time data integration as well as significantly improving their responsiveness and accuracy. Let's explore some of the advantages that make RAG a valuable tool for enhancing the capabilities of LLMs.

Dynamic data integration

Because RAG models continuously integrate data from external knowledge bases into their responses, they provide more up-to-date and relevant information than purely generative models can.  These models verify information from multiple external sources for increased accuracy and customization. 

Customized response Generation

RAG models can customize responses to specific user prompts, allowing for precise answers that are context-aware and better tailored to match the user's query. This in turn can boost user satisfaction and engagement.

Reduced bias and error

By basing their responses in verified, accurate external data sources, LLMs enhanced by RAG retrieval mechanisms minimize the chance of the user receiving inaccurate responses to their queries. If accurate answers are of primary concern, then RAG is a great language model enhancement.

Scalability across domains

RAG's flexible architecture means that it can be used across a wide range of domains--legal, financial, or technical--without requiring extensive retraining. Instead, RAG adapts its retrieval sources to fit the context, making it widely adaptable across different uses and sectors.

Resource efficiency

Unlike conventional LLM architectures, which may evaluate the entire dataset during the generation phase, RAG models scan all available data chunks and then retrieve only the relevant ones. This more targeted approach reduces the computational load during generation, making for quicker response times than those without selective retrieval methods. 

Understanding RAG models

Depending on the specific requirements and goals of the deployment environment, RAG models can look very different in terms of their design and application. Each model uses different ways of retrieving and integrating data based on the particular type of task or industry you're looking at. Let's explore a few of the different types of RAG models along with what makes them unique and their common use cases.

Model 1: Basic RAG

At its most foundational, the basic RAG model retrieves information from a specific knowledge base in response to customer queries. This is most often used in cases where the top consideration is to augment the LLM's responses with up-to-date information, including customer service bots or basic research tools.

Model 2: Domain-Specific RAG

RAG can be used to train LLMs to pull sector-specific data from customized databases, ensuring that its responses reflect the domain knowledge necessary for highly specialized sectors, such as healthcare or legal.

Model 3: Multimodal RAG

A multimodal RAG model can integrate data from formats other than text, including images or even audio. This is especially useful in areas that benefit from a more diverse set of data inputs, such as media analysis or educational tools.

Model 4: Interactive RAG

For more complex use cases that require a conversational back-and-forth with the user, such as a virtual assistant or chatbot responses, interactive RAG models can keep track of the context and history of the interaction to ensure each response is relevant and contextual.

Model 5: Adaptive RAG

In highly dynamic environments where the LLM needs to adapt quickly to new information or updated user preferences, adaptive RAG models allow the LLM to learn from each interaction to improve their retrieval processes over time. This is helpful in scenarios like predictive analytics or personalized content delivery systems.

RAG applications

Data sources for the document store can be any of the following:

• Google docs
• Word docs
• Slack channels
• Notion documents
• Webpages
• …Basically anything that stores free-text data 

Constructing a chatbot to converse with these documents is a versatile use case for RAG. To provide more detailed examples, imagine the following scenarios:

Utilizing internal documentation for employee queries

A new employee at Company A needs to be trained on the onboarding process, company policies, and who to contact for specific needs. Instead of relying on a human HR representative, instead a RAG chatbot––integrated with internal communication tools and document storage platforms––acts as an interactive automated onboarding assistant.

When the employee queries, "What are the steps for completing my onboarding?" the chatbot can retrieve the onboarding checklist in Google Docs and necessary training schedules in Notion. It compiles a personalized list of tasks pulled from information stored in relevant Slack channels.

This speeds up the onboarding process by providing instant, accurate, and personalized information and reduces the workload on human HR representatives, allowing them to focus on more complex issues.

Enhancing customer service with direct product information

Company B manufactures complex machinery that require extensive support documentation and technical manuals. This company could use a RAG-enhanced chatbot to deliver precise technical support, pulling accurate responses from the company's product support section on their website as well as relevant manuals stored as Word docs.

So when a customer queries, "How do I troubleshoot issue X on device Y?" the chatbot can instantly access the relevant troubleshooting steps directly from the latest version of the technical manual. If they need further details or a video walkthrough, the LLM can guide the customer to detailed support articles and videos on Company B's website.

By deploying RAG, customers get real-time, accurate troubleshooting, reducing downtime and improving satisfaction. Using artificial intelligence in this way also decreases the volume of direct support queries handled by human agents to optimize support operations.

RAG versus fine-tuning

RAG is a smart prompt engineering method to improve the accuracy and specificity of LLM-generated responses to context-specific queries. 

Fine-tuning is another popular method used to generate responses from LLMs that are context-specific to a certain set of data. Rather than focusing on prompt-engineering, however, fine-tuning is a method used to further train a foundation LLM on a specific set of data to perform a specific task. This method to improve the accuracy of LLM outputs works best when: 

• You have labeled data
• You want to change the format or style of the responses of an LLM
• You have a static set of training data

There isn’t a clear consensus in the AI community whether fine-tuning is an advantageous method to expand a foundation LLM’s knowledge-base to data that it wasn’t originally trained on. Detractors claim that effectively fine-tuning an LLM would require just as much data and processing power as the pre-training process of the original LLM, at which point you might as well just train your own model.  

Why implement RAG?

The more we looked into RAG, the more excited we got about it as a method to improve and enrich LLM responses. Criticisms of LLMs often include a lack of transparency into the information sources, only being able to reference static data sources, limited context windows, and tendencies toward hallucinations. RAG addresses all of these concerns in the following ways: 

Access to updated information

Unlike traditional LLMs, RAG-augmented models don't have to rely solely on pre-trained datasets, which are quickly outdated. Because they dynamically integrate real-time external data into their responses, RAG models generate responses that are relevant and current (up to the relevant knowledge cut-off date). This ability is especially relevant in fields that change or advance rapidly, including technology, law, and medicine.

Factual grounding

Responses generated by a RAG-enhanced LLM are also far more accurate than a generative model, as they have direct access to verified, updated external knowledge bases. This contextual knowledge gives RAG models greater information integrity––essential in sectors where accuracy matters most, such as journalism, academic research, or customer service guides. In this way, RAG can minimize the risk of hallucinations, misinformation, or inaccurate responses.

Contextual relevance

The retrieval component of a RAG model helps make sure that the user receives only relevant answers to their queries. When managing complex dialogue systems or intricate customer inquiries, this relevance aligns the LLM's retrieved answers more closely with the context of the interaction, producing more accurate and contextually relevant responses.

Factual consistency

RAG models also offer far greater consistency in their responses, since the generation process is conditioned on the external data they retrieve. Reliability is paramount in applications like legal advising or technical support, where a contradictory response or error could have significant repercussions.

Efficient retrieval with vector databases

RAG models that use vector databases organize data into a high-dimensional space, allowing for quick retrieval based on semantic similarity. Traditional retrieval techniques, such as those using keyword matching or sequential database scans, can’t parse large amounts of data quickly for relevant information, making RAG much more efficient in this sense. 

Enhanced conversational chatbots

When integrated with chatbots, RAG allows the bots to access a much broader range of knowledge than just their initial training data. They can then  provide detailed, informative, and accurate responses to much more complex queries.

These advantages show us why implementing RAG in LLMs is beneficial across multiple use cases and scenarios, resulting in more intelligent, reliable, and context-aware AI systems.

By outlining these key reasons, we can see why implementing RAG in LLMs offers substantial benefits across various applications, driving more intelligent, reliable, and context-aware artificial intelligence.

Main Challenges in Implementing RAG

Despite its many advantages, implementing RAG can present challenges. Let's take a look at some of the major hurdles faced when integrating RAG into existing LLMs––and how each of these obstacles affects the deployment and operation of the augmentation itself.

Evaluating the effectiveness of the retrieval system

While RAG augmentation allows an LLM to generate responses based on externally retrieved data, it's not always clear whether this data is in fact the most up-to-date and relevant. Further, there's not a clear method as to how to measure the data's accuracy and contextual relevance, which makes it hard in turn to evaluate the overall system's performance. Without effective evaluation metrics, you may see the quality of the LLM's output degrading over time.

Handling large volumes of data

RAG models trained to retrieve data from especially large knowledge bases or datasets can pose significant technical difficulties. Handling such a large volume of data requires robust architecture and optimization techniques to maintain fast retrieval speed while also ensuring the data's accuracy. These investments can be time-consuming and pricey, but on the other hand, not building this infrastructure can result in bottlenecks in the retrieval process, affecting the LLM's overall usability and user satisfaction.

Integrating external data sources

Setting up the RAG retrieval system to integrate data from a variety of external sources can be complex, resource-intensive, and prone to errors if not managed carefully. Every data source has a different format or structure, for example, which requires complex parsing and normalization techniques before they can be retrieved by the LLM. A further challenge arises from the need to protect sensitive or private data while still making it available for retrieval in a secure manner. All of this means, again, that setting up the RAG model can potentially be expensive and time-consuming.

Balancing retrieval and generation

Another potential pitfall of RAG implementations is achieving the right balance between the retrieval and generation phases. If the retrieval model pulls in data that is irrelevant or too broad, it can have a negative impact on the quality of the generated responses. Conversely, information that is too specific or niche might restrict the LLM from extrapolating or generating flexible, creative responses. Maintaining the right balance between these two sides of the RAG equation requires frequent tuning and adjustment of the retrieval model. 

Managing system costs and scalability

For these and other reasons, RAG models can require more computational resources compared to a traditional LLM, especially when we're talking about retrieving data from larger knowledge bases. This can lead to higher initial setup and ongoing operational costs, especially as the system scales. When deploying RAG on a larger scale, therefore, it's necessary to build the correct infrastructure for managing growing amounts of data while also keeping up with fast retrieval speeds and generating accurate output. Cost considerations, both in terms of hardware and processing power, must be carefully managed to make RAG systems sustainable over time.

Any organization looking to implement RAG in their LLM application must stay ahead of these key challenges to ensure a smooth deployment and optimize performance.