What is a RAG and why is it so powerful?

RAG – Retrieval augmented generation – is an advanced approach in artificial intelligence that fundamentally changes the way information retrieval and processing work. In this article, we explain what RAG is, how it works, the benefits it offers, and the applications it finds in practice.

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What is a RAG – what is retrieval augmented generation?

Retrieval augmented generation (RAG) is an AI technology that combines information retrieval and text generation to produce more precise and contextually relevant answers.

Essentially, RAG uses one or more databases or document repositories to retrieve relevant information and then integrates that information into the text generation. This enables RAG to produce more precise and contextually relevant answers than pure text generation could.

Experts note that “information retrieval” (IR) refers to the field of computer science that deals with finding content.

How does a RAG work?

The functioning of a RAG can be divided into three main steps: information retrieval, information analysis, and text generation.

1. Step: Information retrieval

An input text or question is used to retrieve relevant documents from a large database. These documents are evaluated and filtered based on their relevance.

2. Step: Combination and analysis of information

The retrieved documents are analyzed, and the most important information is extracted. This information is then compiled into a coherent and understandable format.

3. Step: Text generation

The extracted information is used to generate a precise and relevant response or text. The generation is done through an advanced language model capable of creating contextually appropriate and fluent text.

The following RAG infographic illustrates how a user’s input text is used to retrieve relevant documents, how these documents are analyzed, how relevant information is extracted, and finally, how this information is used to generate a precise and coherent text. In this specific case, three quality checks are also carried out: relevance, usefulness, and validity.

The infographic shows the synergy between information retrieval and text generation using ONTEC AI and how it contributes to creating a powerful RAG system.

Infographic showing how a RAG works, using the example of ONTEC AI's RAG

Advantages of RAG

A RAG has specific strengths that can bring significant benefits to companies, including precise answers with fewer AI hallucinations, improved efficiency, scalability, and no need for model retraining. Below is a detailed list of the main advantages of RAG:

RAG provides more precise and relevant answers

RAG systems provide significantly more precise and relevant answers compared to traditional text generation models. This is because they can access a variety of external and/or internal company information and integrate this information in real-time.

RAG increases efficiency

By combining information retrieval and text generation, RAG can increase efficiency in many areas. For example, customer service systems can respond faster and more accurately by retrieving relevant information from a knowledge database and immediately integrating it into the response.

What does efficiency mean in the context of RAGs? Depending on the context, efficiency can have different definitions. What’s described here is the efficiency of employees, which increases with the use of RAG. It is also a fact that the answers from a RAG become proportionally more accurate. Smaller (and therefore more resource-efficient) LLMs can compete with more powerful models (but less resource-efficient and sometimes slower). Thus, a RAG can, for example, be faster and more accurate than a pure LLM into which certain information has been trained.

RAG is scalable

RAG systems are highly scalable and can handle large amounts of data. They can extract relevant information from extensive data sources and use it efficiently.

RAG reduces AI hallucinations

RAG can reduce AI hallucinations because it relies on external, verified data sources rather than relying solely on the internal model knowledge. By retrieving and integrating real-world information from a database, the accuracy and relevance of the generated text are improved, reducing the likelihood of erroneous or fabricated content.

Caution: Just because a source is provided does not mean the model will not still hallucinate. An integrated validity check can prevent an LLM from outputting unsupported information.

RAG provides citations and sources

A retrieval augmented generator can provide citations and references because it retrieves relevant information from external databases and integrates that information into the generated text. This allows for accurate references to the original sources, improving the transparency and traceability of the generated content.

RAG does not require model retraining

RAG allows for refreshing the knowledge base without model retraining, as it retrieves current information from external databases and uses it in real-time. This allows the system to access new data immediately and include it in the generation without needing to retrain the underlying model.

RAG respects RBAC and user rights

RBAC (Role-Based Access Control) ensures that only certain roles have specific rights for operations. RAG respects the RBAC permissions of users by ensuring that only authorized users have access to certain data operations, which ensures data security and integrity.

RAG allows the use of smaller and more efficient models

RAG enables smaller and more efficient models because it uses extensive knowledge databases for retrieving information rather than storing all the knowledge in the model itself. As a result, the model can be smaller and less complex, as it does not need to retain all the information internally but can retrieve it externally when necessary.

RAG for standardized models while maintaining competitive differentiation

A RAG makes the non-retrained model competitive or even better in terms of performance, specifically in terms of the percentage of correct answers.

At the same time, the competitive differentiation is maintained through the use of different data sources and specific retrieval strategies. This allows companies to bring in their unique strengths and domain knowledge while benefiting from the efficiency and stability of a standardized model. Additionally, the investment in retraining can be avoided, making RAG a more cost-efficient solution.

Stumbling upon any unknown terms in this article?

We have compiled the basic vocabulary of artificial intelligence in our AI glossary.

Challenges of retrieval augmented generation

Like any IT system, a retrieval augmented generator comes with specific challenges. Anyone considering a RAG should address the following areas:

Data quality and availability

One of the biggest challenges in implementing RAG is the quality and availability of the data. RAG systems rely on extensive and high-quality databases to retrieve accurate information.

What does high-quality data mean here?

  1. No outdated or incorrect information in the documents
  2. The documents must be correctly read by the machine without issues like file format or image quality hindering the process.

A strength of RAG is that it can even work with a single page of content. Of course, the answers are dependent on the available content. What is not included will not be answered.

Integration and adaptation

Integrating RAG into existing systems can be complex and requires careful adaptation to the specific needs of the application. This requires both technical expertise and a thorough analysis of use cases and existing data formats.

With advancing technology and the availability of larger and higher-quality databases, RAG systems are expected to become even more powerful and versatile in the future.

Summary and key takeaways

Retrieval augmented generation (RAG) is an innovative technology that combines information retrieval and text generation to produce more precise and relevant answers. It offers significant benefits in terms of accuracy, efficiency, and scalability and is used in various fields such as customer service, knowledge management, insurance, and healthcare.

Despite some challenges, such as the need for high-quality data and integration into existing systems, RAG has the potential to fundamentally improve our information processing and usage.