What Is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is a technique that combines a language model with an external retrieval system, such as a database, search engine, or collection of documents.
In other words, retrieval-augmented generation is a hybrid approach that enables a generative AI system to access and extract data from an external source, rather than relying solely on the data on which it was trained.
When a user enters a prompt into such an AI system, the system first retrieves relevant information from an external source before passing the prompt and retrieved data to the large language model (LLM), which uses them, alongside its own training data, to generate a response.
The external retrieval system from which the generative AI system extracts information can be open-source, such as the web, or a closed system, such as databases that are only accessible to authorized individuals.
Importance of Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) helps to ensure that the generative AI system returns relevant answers to the user’s queries, even when such data is absent from its training data.
This enhances the accuracy and capability of the generative AI system and is crucial for resolving multiple AI issues, such as the cut-off date problem.
Generative AI systems, such as ChatGPT e Gemini, have a cut-off date, which refers to the last date their training data was updated. This means the AI system will not have any information beyond that date, as it simply does not exist in its database.
For example, an AI system with a cut-off date of January 2025 will be unable to answer questions about an event, such as a football match, that occurred in April 2025. Similarly, the AI system cannot tell the weather or even the stock prices of the days following its cut-off date.
However, with retrieval-augmented generation, the AI system can access such information and then return it to the user.
In the case of ChatGPT and Gemini, retrieval-augmented generation enables them to search the web in real-time and extract the necessary information for the user. This improves the accuracy of their responses and even allows them to behave like search engines, which makes them more helpful to the user.
How Retrieval-Augmented Generation Works
Retrieval-augmented generation (RAG) is a two-step process that involves retrieving data from an external data source and generating a response, which is then returned to the user.
When a user enters a prompt into the AI system, it retrieves relevant information from an external source. This “external source” could be a private database, document, internal webpage, or publicly available webpage.
Once done, the system then proceeds to pre-preprocess the data. That is, it tokenizes the words, stems them, and removes stop words.
In artificial intelligence, tokenization is the process of breaking sentences into words, subwords, or characters, while stemming is the process of reducing words to their root or base form. For example, “running” can be stemmed into “run.”
Stop words, for their part, are words like “the”, “is”, and “and”. They do not add meaningful value to the content and are often used frequently in everyday language.
Once done, the AI system generates a response using its own large language model as well as the retrieved data.
Benefits of Retrieval-Augmented Generation
Retrieval-augmented generation improves a generative AI’s system ability to return relevant answers to users. This has multiple benefits for the AI system and its users.
1 It Improves Accuracy
Retrieval-augmented generation allows AI systems to incorporate the latest facts, discoveries, news, and updates into their responses. This is particularly useful in fast-changing niches, such as finance, health, or news, where users typically require factual and up-to-date information.
2 It Reduces Hallucination
Hallucination refers to the situation wherein an artificial intelligence model generates false or misleading information. This occurs when the AI model tries to answer a question using incomplete, outdated, or nonexistent information from its training data.
Retrieval-augmented generation reduces an AI’s ability to hallucinate, as the AI system can complement its data with that extracted by the system. This keeps its response up-to-date and reduces the impact of incomplete and outdated data on the response.
3 It Allows the Usage of Smaller AI Models
Retrieval-augmented generation systems enable businesses to utilize smaller AI models than they would with a standard generative AI system that lacks retrieval-augmented generation capabilities.
This is helpful for businesses that require a generative AI system but do not want to invest heavily in training or deploying large, resource-intensive models.
Instead of investing in a resource-intensive model, the business can instead rely on a smaller and less resource-intensive model with retrieval-augmented generation capabilities.
4 It Reduces the Size of the AI Model
Retrieval-augmented generation reduces the size of the AI model. This, in turn, reduces the resources required to create, train, and maintain it.
This works because retrieval-augmented generation enables the AI model to store data in an external database rather than its memory. This separation of storage from generation keeps the AI system lighter than it would otherwise be.
5 It Is Easier to Update
Retrieval-augmented generation makes it easy to update your generative AI system. Instead of retraining the AI model as you would with a non-RAG system, you only have to update the external database with the required information, and the AI will have access to it.
6 It Allows AI to Access Internal Data
Retrieval-augmented generation systems can be configured to only search within a specific data or knowledge base. This includes private databases that are not accessible to outsiders.
This is helpful as it allows the AI to access private information without compromising the data.
Without the retrieval-augmented generation system, the AI would have to store the data in its own memory, which leaves it vulnerable to data leaks, unauthorized access, and unintentional exposure of sensitive information to third parties or even the public.