Retrieval augmented generation (RAG)

Retrieval augmented generation (RAG) is an artificial intelligence (AI) architecture that incorporates external knowledge sources to enhance the capabilities of large language models (LLMs). In other words, it makes LLMs more powerful by combining them with the ability to retrieve real-time data. RAG pulls in relevant information from outside databases and augments LLM input so that the output can include more relevant, accurate, and contextually appropriate responses. As such, retrieval augmented generation (RAG) has positioned itself as a cornerstone of enterprise AI applications. 

Some examples of how it is used include: 

Customer support: Provide consistent, AI-generated answers backed by product documentation or help center articles.

Enterprise search: Let employees ask natural language questions and receive answers based on internal documentation.

Legal or compliance: Summarize or extract answers from large volumes of policy documents.

Healthcare (and other heavily regulated industries): Deliver accurate, traceable insights grounded in verified sources.

How does retrieval augmented generation (RAG) work?

RAG taps into outside data sources, including data repositories, databases, and documents to expand LLM information beyond training data. It’s an AI technique that enhances a language model’s performance by first retrieving relevant documents or data from an external source (like a knowledge base or document store) and then generating a response based on both the retrieved data and the model’s internal knowledge. In simple terms, RAG enables the model to “look things up” before answering.

Specifically, there are two primary steps: 

Retrieval Step: When a user asks a question, the system searches a large set of documents (such as PDFs, FAQs, web pages, or databases) using a retriever model, often based on semantic similarity or keyword matching. It selects the most relevant pieces of content.

Generation Step: The retrieved documents are passed to a generator model (like GPT or similar), which uses them to craft a coherent, contextually accurate response.

In this way, the responses are plausible and grounded in real data.  

The importance of retrieval augmented generation (RAG)

LLMs are typically trained on data that eventually goes stale. For example, a model trained in 2020 can’t know about anything from 2021 unless it can access updated information. Retrieval augmented generation (RAG) solves that limitation by enabling the model to access real-time or proprietary information. It can then answer questions about recent updates or niche domains that weren’t included in its training data. 

The benefits of this dynamic knowledge are numerous: 

• Accurate: LLMs fed relevant information from external sources provide more accurate responses than those that are not.
• Contextually relevant: Context-specific information that is incorporated into LLMs enables them to respond to the user's query in a more relevant way. 
• Recent: While training data can become stale, augmenting with up-to-date information makes LLMs more useful. 
• Cost-effective: As organizations and businesses look to adapt LLMs to their unique needs, RAG is a cost-effective way to tailor the system to a company’s internal data without retraining the entire model.

Retrieval augmented generation (RAG) closes the divide between pre-trained AI models and current, dynamic knowledge. This empowers organizations to build AI systems that are both conversational and trustworthy, expanding opportunities for customer service, research, knowledge management, and more.

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