What is Retrieval-Augmented Generation (RAG)?

As artificial intelligence becomes increasingly present in our lives, you may have come across RAG – but what is retrieval-augmented generation? In this post, we’ll discuss what retrieval-augmented generation is in AI, examples in practice, and the pros and cons so you can make the right decisions when creating advanced business strategies. Read on for more information.

Retrieval-Augmented Generation Explained: What is RAG?

Retrieval-augmented generation, otherwise known as RAG, is a process for optimising a large language model’s (LLM) output. It uses external sources outside of its training data sources to boost the accuracy of AI models.

What Is Retrieval-Augmented Generation (RAG) Primarily Focused On?

Simply put, retrieval-augmented generation bridges a gap in how LLMs operate. LLMs are neural networks measured by their parameters, which represent patterns of how humans use words. This understanding makes LLMs valuable in creating responses to general prompts, but it is not useful to users who would like more detailed information on specific topics.

LLM challenges include:

• Offering incorrect information when the model cannot find the correct answer.
• Failing to offer answers to prompts that are too current. For example, it won’t know what happened in the news yesterday!
• Presenting an incorrect response because of confusion with terminology. For example, various training sources may leverage the same terminology to discuss different things.
• Generating a response from sources which are deemed non-authoritative. Users may only want an answer created from sources seen as credible.

Therefore, retrieval-augmented generation is important as it aims to compensate for LLMs’ drawbacks, helping users get the accurate and precise information they seek.

Retrieval-Augmented Generation Architecture: How Does RAG Work?

As we’ve already discussed, RAG aims to make up for LLM shortcomings by pulling information from external data sources rather than crafting responses based on input information the model was trained on. This means the LLM can utilise new and valuable knowledge combined with its training data to generate more useful responses. So, how does augmented generation retrieval work? Let’s delve into a little more detail below.

1. Processing the input

The journey begins with user input, typically a question or prompt, which the model can then process.

2. Retrieval

The input is encoded into a query vector utilising a neural network. 

3. Utilising external data

Following this, it is used to search a wide range of documents and identify relevant content.  External data is any data outside of the LLM’s training data set. It comes from external databases, APIs, and other sources and is stored in a knowledge library that generative AI models are trained to understand.

4. Supplementing the prompt

The RAG model supplements the LLM prompt by adding relevant external data in context. Utilising prompt engineering techniques, the RAG model communicates efficiently with the LLM, allowing the model to produce an accurate response to user questions.

5. Updating the external data

Data is always at risk of becoming outdated. Thus, you can update the documents and document embedding representation to avoid producing out-of-date answers to more current events. This can be conducted through one of two ways: periodic batch processing or automating real-time processes.

What Are the Benefits of RAG?

If you’re wondering whether retrieval-augmented generation is right for your business, we recommend weighing up the advantages and disadvantages. Here are some of the most common benefits of RAG:

• Up-to-date information

Maintaining relevancy in language models can be tricky, but RAG in AI offers the most updated statistics, research, and other information to generative models. With this, LLMs can offer users the latest and most relevant information, which is particularly useful to those looking for news updates or information on current events.

• Cost-efficient

The cost of retaining Foundation Models for businesses can be expensive. RAG can help save you money by simply introducing new, valuable data to the LLM. As such a cost-effective solution, generative artificial intelligence technology is now becoming more accessible to businesses that may have smaller budgets.

• Enhanced control

Developers using RAG can test and enhance chat applications more easily, controlling and modifying LLM data sources to adapt to ever-evolving requirements. Furthermore, the developer can limit sensitive information retrieval to different authorisation levels, ensuring the LLM creates relevant responses. If the LLM does use incorrect data sources, developers can also fix issues efficiently, meaning that organisations can implement and use AI technologies more confidently overall.

• Increased trust

The retrieval-augmented generation architecture allows LLMs to offer more accurate information from a wide range of sources, all of which can be looked up manually. With this in mind, users are likelier to trust the information and gain confidence in this AI solution. Building trust with your audience can keep them coming back for more.

What are the Problems with Retrieval-Augmented Generation?

While RAG has plenty of benefits, you must also consider the drawbacks before implementing AI solutions.

• Ethical concerns

Content created by AI requires a careful approach, especially as skilled workers have started to fear for their livelihoods. Thus, ethical guidelines could help navigate the development and implementation of RAG.

• Accuracy concerns

Whilst RAG models can improve the accuracy of LLMs, they can still produce incorrect information. To maintain high-quality responses, RAG systems should be tested thoroughly and subjected to verification processes.

• Bias concerns

RAG’s dependence on existing database information could introduce biases into the content it creates. Therefore, it is important to present measures that detect and eliminate bias in RAG-generated content. 

Retrieval-Augmented Generation: Examples of When It Should Be Used

So, what is retrieval-augmented generation used for? Now that you know what RAG is and how beneficial it can be, take a look at examples of retrieval-augmented generation to get a better idea of when it could be used as an effective tool for your company.

• Search augmentation for employees

RAG capabilities ensure better responses to informational queries, using company data as a context for LLMs. This ensures users can access the accurate information they need to do their jobs more efficiently, whether they need to get quick answers to HR, compliance, or security questions. This can be spread across various industries, from computing to marketing to healthcare.

• Chatbots for customers

When it comes to customer service, question-and-answer chatbots are now a standard. Chatbots that allow LLMs to instantly present more accurate answers from knowledge bases and business documents can help streamline customer support and increase leads by resolving issues rapidly. In turn, this could boost satisfaction and loyalty, which is critical for retail industries as well as a wide range of other sectors.

• Market research for professionals

Since RAG can help gather information from huge volumes of data on the internet, it can keep your company up-to-date on market trends and competitor activities to better inform your strategic decisions. By scrutinising industry reports, social media posts, and current news articles, you can discover relevant topics so you can always provide a product or service that your target consumers are interested in.

• Content generation for businesses

With RAG, content creators can stay one step ahead of the game. They can combine generative AI capabilities with reliable information sources to assist in producing blog posts and articles at rapid speed, saving time that can then be spent on other aspects of their business.

• Sales support for organisations

RAG can operate as your virtual sales assistant by efficiently addressing customer queries. For example, it can quickly retrieve information about product specifications and explain product manual instructions. With such a wide breadth of information at its fingertips, RAG can provide customers with tailored recommendations and address specific problems to improve the overall shopping experience. In turn, this personalised touch and efficient solution can keep customers coming back for more.

To see how we have applied AI solutions to previous clients, check out our case studies. 

Our Round-Up on AI RAG

So, that’s a wrap on RAG in AI! If you think you would benefit from AI development services, please don’t hesitate to reach out to us. One of our experts will be happy to guide you through our process so you can better understand how we can help your business grow.If you would like to find out more information concerning artificial intelligence, be sure to check out our blog. Here, you can find valuable content on subjects like how AI can improve customer experience and how we create AI assistants.