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Feb 17, 2025

11 minutes read

What is RAG architecture? An emerging approach to LLMs

Retrieval-augmented generation (RAG) is an evolving approach to interacting with LLMs. Learn about the benefits and use cases of RAG architecture.

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_Updated: December 11, 2025_

While enterprise AI has been readily adopted by some businesses seeking to improve efficiency and productivity, others have been more reticent. They’re averse to the risks inherent in large language models (LLMs), such as the need for regular knowledge updates to maintain their accuracy, the possibility of hallucinations (incorrect outputs), security and compliance concerns, as well as overall costs.

Fast-emerging as a way to mitigate these risks is retrieval-augmented generation (RAG) architecture, an effective approach to enhancing how applications interact with LLMs.

Through RAG architectures, organizations can connect their LLMs to external databases. This helps give the model access to additional context through dynamic information retrieval methods, adding more power and flexibility to an enterprise AI ecosystem and helping to mitigate some of the risks around accuracy and trustworthiness of generated answers.

Let’s take a look at what RAG is in more depth, its main benefits, some use cases for enterprise RAG architecture, and how the technology may evolve over time.

##### What is RAG architecture in LLMs?

RAG architecture describes a framework intended to improve generative AI models by integrating methods for enhanced retrieval. The resulting AI ecosystem enjoys the benefits of both generation-based and retrieval-based systems as it crafts responses to queries.

Traditional LLMs can only generate responses based on their training data. This can result in knowledge cut-offs that increasingly risk the accuracy of generated answers as time goes on, reducing the reliability of the AI tool at hand.

Advanced RAG architecture, on the other hand, adds the capability to retrieve and integrate external and newer data sources during the answer generation process. This can improve the accuracy of query responses and also help ensure responses are more contextually relevant.

##### RAG architecture explained: The different components

Under the hood, LLM RAG architecture consists of several interlinked components, each playing an important role in the end-to-end process.

###### The RAG retrieval component

Once a user inputs a specific query, the retrieval component in a RAG system is responsible for the search and retrieval of relevant data from any of the large and varied datasets that have been connected to the LLM. This process ensures that the system can access accurate and contextually-appropriate information.

The retrieval method can vary depending on the build and purpose of the system. For instance:

• Traditional keyword-based retrieval identifies information by matching specific terms in the query with data in the dataset.
• Dense retrieval employs embeddings—mathematical representations of text—which give the model semantic understanding. This enables the system to find information based on meaning rather than exact keyword matches.

###### The RAG encoder component

Once information is retrieved, it’s then processed by the encoder component. By transforming text data into dense vector representations, the encoding helps the model to contextualize the retrieved information in the next phase.

###### The RAG generation component

Once the retrieval component has gathered the necessary data, the generation component takes over. It uses a generative AI model to create a response based on the retrieved information.

This component ensures the output is coherent, relevant, and contextually aligned with the input query. Unlike the retrieval component—which focuses on finding relevant data—the generation component interprets the retrieved data and crafts a meaningful response. Its role is distinct but interdependent, as it relies on the quality and relevance of the information provided by the retrieval process.

##### Elements of the extended RAG ecosystem

###### The RAG training process

The training process allows for end-to-end optimization of the RAG system, simultaneously refining both the retrieval and generation processes. This integrated training approach can enhance the system's accuracy, consistency, and performance.

By enabling the components to learn collaboratively, the training process helps ensure that retrieval strategies and generation models are better aligned, ideally leading to improved responses. It focuses on system improvement rather than real-time operations.

###### The RAG application layer

This layer represents a stage of deployment of RAG, or its practical implementation. It is where RAG-powered applications are put into practical use for specific tasks. For example, in the context of RAG for enterprise, this includes customer support, deep information retrieval, or other AI-driven workflows that need both retrieval and generation.

Unlike other components focusing on optimization and system architecture, the application layer bridges the technology with real-world use.

##### Example use cases of RAG architecture

RAG architecture bridges LLMs with advanced retrieval capabilities, delivering functionality to enable dynamic access to external knowledge sources. This has applications across industries, helping to solve differing operational challenges.

###### Energy and utilities

• RAG systems can support engineering teams through better access to diagnostic data, maintenance logs, and technical manuals.
• By retrieving historical maintenance logs, fault records, and operational data, RAG systems can generate maintenance schedules or failure predictions. This helps enable energy and utilities providers to prioritize equipment servicing and minimize disruptions to energy supply.
• RAG systems can...