Retrieval-Augmented Generation (RAG)

What is Retrieval-Augmented Generation (RAG)?

RAG stands for Retrieval-Augmented Generation, an AI development technique where a large language model (LLM) is connected to an external knowledge base to improve the accuracy and quality of its responses.


Types of sources that LLMs can connect to with RAG include document repositories, files, APIs, and databases.

Techopedia Explains the RAG Meaning

Retrieval-Augmented Generation (RAG)

LLMs use Retrieval Augmented Generation to be able to extract information from an external knowledge base. This provides the model with access to up-to-date, domain-specific information, which it can reference when responding to user prompts in real time.

One of the main advantages of this approach is that the knowledge of the model isn’t confined to training data with a particular cutoff date. The knowledge base can also be updated without needing to retrain the model.

Having access to an external resource reduces the chance of hallucinations, where an LLM produces a verifiably false or untrue output. At the same time, the clear link to a knowledge base makes it easier for users to view and fact-check sources for the chatbot’s claims.

Now that we’ve set out a retrieval augment generation definition, let’s look at how it works.

How Does Retrieval-Augmented Generation Work?

How RAG Works
Source: blog.mindmeldwithminesh

At a high level, RAG has two main phases; a retrieval phase and a content generation phase.

During the retrieval phase, a machine learning (ML) algorithm uses natural language processing (NLP) the user’s prompt and uses this to identify relevant information from its knowledge base.

This information is then forwarded to a generator model or LLM, which uses the user’s prompt and the data compiled throughout the retrieval phase to generate a relevant response that matches the original prompt intent. The process relies on natural language generation (NLG).

History of RAG

The term Retrieval Augmented Generation was originally coined in a research paper titled Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, produced by researchers from Facebook AI Research, University College London, and New York University.

This paper introduced the concept of RAG and outlined how it could be used in language generation tasks to produce more specific and accurate outputs.

“This work offers several positive societal benefits over previous work: the fact that it is more strongly grounded in real factual knowledge (in this case Wikipedia) makes it “hallucinate” less with generations that are more factual and offers more control and interpretability,” the paper said.

In addition, the research noted that “RAG could be employed in a wide variety of scenarios with direct benefit to society, for example by endowing it with a medical index and asking it open-domain questions on that topic, or by helping people be more effective at their jobs.”

RAG Architecture

RAG architecture has a number of core components that enable it to function. These are as follows:

Web Server/Chatbot

The web server hosts the chatbot interface where users can interact with the language model. Prompts are passed to a retrieval model.

Spoiler title

This knowledge base/data storage component contains files, images, videos, documents, databases, tables, and other unstructured data that the LLM will process to respond to user queries.

Retrieval Model

The retrieval model analyzes the user’s prompt with NLP and searches for relevant information in its knowledge base, before forwarding it to the generation model.

Generation Model

The generation model processes the user’s initial prompt, and then information is collected with the retrieval model to generate a response which is sent to the user via the chatbot interface.

Use Cases of RAG

RAG Use Cases

RAG offers lots of potential use cases for enterprises. We’re going to look at some of the most significant below:

  • Building Document Research Assistants: Using RAG enables organizations to build chatbots that employees can use to query data stored in company documents. This is useful for answering technical questions on HR, compliance, and security topics.
  • Customer Support: Businesses can also use RAG to create customer support chatbots that provide users with access to more accurate and reliable information. For example, a retailer could develop a chatbot that’s prepared to answer user questions about delivery and returns policies.
  • Content Generation: Marketers can use RAG to build domain-specific LLMs which can create content, such as articles, blog posts, and newsletters, which are tailored toward the needs of a particular target audience.
  • Industry Analysis: Decision-makers can also use language models with RAG to create market analysis reports. For instance, the user can add market data and industry reports to a knowledge base and then ask a chatbot to summarize the key trends.
  • Healthcare Guidance: Healthcare providers can use RAG to build chatbots that can provide patients with access to medical information and support. This can help to offer 24/7 patient care when a physician isn’t available.

RAG Challenges

While RAG is an extremely useful approach to AI development, it isn’t perfect. Perhaps the biggest challenge with using RAG is that a developer needs to build an extensive knowledge base of high-quality content for reference.

This is a difficult process because the data needs to be carefully curated. If the quality of the input data is low then this will negatively affect the accuracy and reliability of the output.

Likewise, developers also need to consider whether the knowledge base has any biases or prejudices that need to be addressed.

Finally, while RAG can help increase reliability, it can’t eliminate the risks of hallucinations entirely, so end users still need to be cautious about trusting outputs.

Pros and Cons of Retrieval-Augmented Generation

As a technique, RAG offers organizations a wide range of pros and cons. Below we’re going to look at some of the top advantages and disadvantages it has to offer.


  • Connecting to a domain-specific knowledge base ensures more precise information retrieval and reduces misinformation
  • Updating the knowledge base instead of retraining the model saves time and money for developers
  • Users gain access to citations and references, facilitating easy fact-checking
  • Domain-specific outputs meet users’ specialized needs more effectively


  • Without high-quality data, output quality may suffer
  • Building a substantial knowledge base demands significant time and organization
  • Biases in training data can influence outputs
  • Even with improved accuracy, there remains a risk of hallucinations

The Bottom Line

RAG is a valuable technology for enhancing the core capabilities of an LLM. With the right knowledge base, a developer can equip users with access to a mountain of domain-specific knowledge.

That being said, users still need to be proactive about fact-checking outputs for hallucinations and other mistakes to avoid misinformation.


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Tim Keary
Technology Specialist

Tim Keary is a freelance technology writer and reporter covering AI, cybersecurity, and enterprise technology. Before joining Techopedia full-time in 2023, his work appeared on VentureBeat, Forbes Advisor, and other notable technology platforms, where he covered the latest trends and innovations in technology. He holds a Master’s degree in History from the University of Kent, where he learned of the value of breaking complex topics down into simple concepts. Outside of writing and conducting interviews, Tim produces music and trains in Mixed Martial Arts (MMA).