What is Retrieval Augmented Generation (RAG), and why should you use it in your company?
Kamil Koligot
AI Engineer
25. September 2023
In the age of big data and artificial intelligence, Large Language Models (LLMs) are changing how we analyze and interact with our data resources. They create new opportunities for more intelligent data-processing techniques. They can help users analyze documents more quickly using complex questions, making knowledge acquisition and decision-making processes easier and more efficient.
With businesses and organizations gathering large volumes of data and documents, it becomes clear that we need better tools to manage, search, and effectively use all this information. Retrieval Augmented Generation (RAG) is a powerful approach that provides Large Language Models (LLMs) with additional knowledge by enriching prompts with relevant context retrieved from external data sources. These solutions enable employees to locate information quickly without manually navigating through large document collections.
In this article, we will delve into how a RAG system works, explain its components, highlight the main advantages it brings, and discuss the potential challenges that may arise along the way.
What is Retrieval Augmented Generation (RAG)?
Retrieval Augmented Generation (RAG) is a method that enhances prompts by including contextual, relevant information from external sources. This technique significantly improves the abilities of Large Language Models (LLMs) when working with domain-specific or proprietary data.
Although models from the GPT family and other modern Large Language Models (LLMs) are very advanced, they rely on static training data. This means they do not automatically incorporate new information or have access to an organization’s internal knowledge.
RAG addresses this limitation by combining information retrieval with text generation. In simple terms, it allows an AI system to generate content based on what it finds in a custom document collection or database. The goal is to merge the general language understanding of Large Language Models (LLMs) with fresh, domain-relevant knowledge.
Before RAG became widely used, improving a model’s performance in a specific domain typically required fine-tuning. However, fine-tuning is time-consuming, costly, and must be repeated whenever new data is introduced.
RAG takes a different approach. It dynamically enriches each prompt with context from the latest available documents. This provides a continuous knowledge boost and reduces the need for repeated model retraining, saving time and computational resources.
We can distinguish two main parts of the RAG approach:
- the retrieval phase
- the generation phase
Retrieval phase
In the retrieval phase, after receiving a query, the system searches a text corpus or database to find information relevant to the question. Increasingly, modern RAG systems rely on semantic search instead of keyword matching, enabling them to identify meaningfully related content even when wording differs. Vector databases play a crucial role in enabling efficient similarity search across large collections of documents.
Generation phase
In the generation phase, the system generates an answer based on the retrieved text. The Large Language Model (LLM) combines the information it found with the foundational knowledge obtained during its training, resulting in more accurate and context-aware responses.
As shown in the diagram above, the RAG process begins with a user question, which is then transformed into a form suitable for efficient semantic search. Next, the system searches for the most relevant information in a predefined collection of texts or a database.
When the system searches for information, it goes beyond keyword matching. Instead, it uses semantic search to find related information more effectively. Think of it like searching a library by topic rather than by title. Tools like vector databases make this possible at scale.
The user’s question and the retrieved text passages are then combined so the model can generate the final answer based on both training knowledge and domain-specific context.
You can learn more about vector databases here.
Advantages and challenges
One of the key benefits of RAG systems is their dynamic knowledge base. Since the retrieved context comes from external documents, the system can reflect knowledge updates instantly. RAG systems are widely used in customer service, internal knowledge search, and content generation where accuracy and up-to-date information matter.
A practical real-world example is our collaboration with RPP Group, a public-affairs consultancy handling large volumes of political and policy-related documents. By integrating a RAG-based chatbot into their workflows, RPP Group was able to automate parts of their research, analyze documents more quickly, and receive grounded answers based on their internal knowledge sources. This significantly reduced manual work and improved the speed and reliability of their daily operations. Read more about our RAG Customer story: Read More.
There are also challenges in building a RAG system. Ensuring that retrieved information is relevant is essential. Retrieval quality heavily influences output accuracy. The retrieval step often requires additional components such as vector databases, which increase system complexity and may affect response time. Effective RAG development requires experience in information retrieval, embedding models, and data preparation.
Conclusion
Retrieval Augmented Generation (RAG) offers a new and powerful way to enhance Large Language Models (LLMs) with dynamic, custom knowledge. By combining the model’s general capabilities with freshly retrieved information, RAG enables more accurate, reliable, and flexible AI solutions.
As organizations increasingly adopt AI tools, RAG represents a promising method to improve the relevance of responses, support decision-making, and develop more robust business applications.
If you’re interested in integrating the RAG system into your business operations, please don’t hesitate to reach out to us. We’re here to assist you.
