Retrieval-augmented generation (RAG) is a technique that improves the accuracy of AI-generated responses by combining a language model – a system trained to generate human-like text – with an external retrieval system that searches for relevant information from knowledge sources. Rather than relying solely on what it learned during training, a RAG model retrieves supporting documents before generating an answer, making its responses more grounded and reliable.

RAG builds on large language models (LLMs), which generate complex and context-aware text using vast datasets. However, because LLMs have static knowledge limited to their training data, they can sometimes produce outdated or inaccurate responses. RAG addresses this by incorporating a retrieval step that fetches relevant information before creating a response. These retrieval sources can vary widely – from an organization’s internal documents to broader knowledge bases, specialized databases, or even the open web.

This approach is especially helpful in areas where up-to-date and accurate information is critical. By using both memory and fresh information, RAG systems can better give trustworthy and relevant answers.

How does retrieval-augmented generation (RAG) work? 

By connecting language models to external information sources, RAG creates a powerful system that delivers more accurate, factual, and up-to-date answers. Here’s how it works:

1. Retrieving information from external sources

RAG searches a collection of knowledge sources, such as documents, databases, and websites, instead of relying solely on pre-trained knowledge. To make retrieval efficient, this information is typically stored in a format that allows AI to find relevant content even when the wording differs from the user’s query. One method used for this is semantic search, which retrieves information based on meaning rather than exact keywords.

2. Finding the most relevant data

Before generating a response, RAG identifies and ranks the most relevant information from its sources. It does this by comparing the meaning of the user’s query to stored data using text embeddings—numerical representations of words and phrases that allow the system to understand similarity in meaning. These embeddings are stored in vector databases, which are specialized systems designed to quickly find and retrieve the most relevant content. This process helps reduce the likelihood of incorrect or irrelevant information appearing in the AI’s response.

3. Structuring the response

Once relevant information is found, it is combined with the user’s query in a structured way before being sent to the language model. This process, called prompt construction, helps the AI understand which details to prioritize. AI engineers use prompt engineering techniques to ensure the format is clear and relevant, improving the quality of the final output. Since language models can only process a limited amount of information at once (due to context window limits), the retrieved content must be carefully selected to fit these constraints while maintaining accuracy.

4. Generating the answer

Once the relevant information is organized, the language model generates a response that blends the retrieved facts with its pre-trained knowledge. A key challenge is ensuring these two sources of information complement each other without introducing contradictions. If conflicting data is retrieved, RAG must assess which source is most reliable to produce a coherent and trustworthy response.

5. Keeping information up to date

To stay relevant, RAG systems continuously update their knowledge sources. This can happen in real-time for dynamic domains, such as financial markets, or periodically for more stable fields, like academic research. By ensuring language models always have access to the latest information, RAG helps overcome a major limitation of traditional large language models, making them more adaptable, accurate, and reliable.

What is RAG used for?

Retrieval-augmented generation (RAG) has quickly become essential for enterprise AI applications requiring factual precision. Many companies are now adopting RAG because it helps reduce AI mistakes and makes responses more trustworthy—especially when it’s well-implemented and connected to reliable data sources.

Here are some ways people use RAG today:

  • Enterprise knowledge and documentation: RAG helps employees quickly find information in company documents. Instead of searching through folders or sending emails, they can ask questions naturally and get accurate answers from employee handbooks, policy guides, and training materials.
  • Customer support and AI assistants: Customer service chatbots use RAG to pull answers directly from product manuals and support guides. Customers get correct information without waiting for a human agent, while still having the friendly conversational experience of talking with an AI.
  • Research and healthcare applications: In healthcare, RAG systems help doctors access the most current treatment guidelines. Researchers use RAG to search through scientific papers and bring together relevant findings, saving countless hours of manual research.
  • Finance and market analysis: Financial advisors create reports that combine AI analysis with real-time market data. Investment teams use RAG systems to stay on top of changing regulations and market conditions when assessing potential risks.