Retrieval augmented generation (RAG)

What is retrieval-augmented generation (RAG)?

Retrieval-augmented generation (RAG) is a natural language processing (NLP) technique that combines the strengths of both retrieval- and generative-based artificial intelligence (AI) models. RAG AI can deliver accurate results with  pre-existing knowledge but can also process and consolidate that knowledge to create unique, context-aware answers, instructions, or explanations in human-like language rather than just summarizing the retrieved data.

An AI RAG model differs from generative AI in that it is a superset of generative AI. RAG combines the strengths of both generative AI and retrieval AI. RAG is also different from cognitive AI, which mimics the way the human brain works.

How does RAG work?

RAG works by integrating retrieval-based techniques with generative-based AI models. Retrieval-based models excel at extracting information from pre-existing online sources like newspaper articles, databases, blogs, and other knowledge repositories such as Wikipedia or even internal databases. However, such models cannot produce original or unique responses.

Alternatively, generative models can generate original responses that are appropriate within the context of what is being asked, but can struggle to maintain strict accuracy.

RAG was developed to overcome these relative weaknesses in existing models to combine their strengths and minimize drawbacks.

A retrieval model finds relevant information from existing sources in a RAG-based AI system. In contrast, the generative model takes the retrieved information, synthesizes all the data, and shapes it into a coherent, contextually appropriate response. 

In practice, the process looks something like this: A user submits a query in natural language. The system converts that query into a vector representation using an embedding model, then compares it against indexed content in vector databases. This process looks for semantic similarity rather than relying on exact keyword matches.

The system retrieves the most relevant documents or passages, often ranking them again for retrieval quality, and adds the retrieved context to the model prompt. The LLM uses that additional context and the user input to produce a final response. The strength of the result depends heavily on accurate retrieval, the quality of the underlying data sources, and how accurately the retrieved documents answer the user’s question.

What are the benefits of RAG?

By integrating information retrieval and generative artificial intelligence (AI) models, RAG delivers more accurate, relevant, and original responses that sound human. That’s because RAG models can understand query context and generate fresh, unique replies by combining the best of both models.

• More accuracy — A retrieval model first pulls relevant information from existing knowledge sources. The generative model then uses that information to write its response, which makes the answer more relevant and up to date than one from a generative model working alone.
• Better synthesized information — By combining retrieval and generative models, RAG can synthesize information from numerous sources and generate fresh human-like responses. This is particularly helpful for more complex queries that require integrating information from multiple sources.
• Putting information into context — Unlike simple retrieval models, RAG can generate more relevant, context-aware responses It retrieves passages related to the user's query, then passes them to a language model that writes an answer grounded in that material.
• Trainability — Training an NLP-based large language model (LLM) to build a generative AI model requires a tremendous volume of data. Alternatively, RAG models use preexisting and pre-retrieved knowledge sources, reducing the need to find and ingest massive amounts of training data.
• Efficiency — RAG models can be more efficient than large-scale generative models, as the initial retrieval phase narrows down the context and thus the volume of data that needs to be processed in the generation phase. RAG also provides teams with a more manageable path to current answers. Instead of retraining a model whenever policy, product documentation, customer records, or other raw data changes, organizations can update the relevant data in the retrieval layer. This helps support up-to-date responses while retaining governance over which external sources and internal content an application can access.

RAG use cases

These are some real-life examples of how RAG models are being used today to:

• Improve customer support: RAG can be used to build advanced chatbots or virtual assistants that deliver more personalized and accurate responses to customer queries. This can lead to faster responses, increased operational efficiencies, and greater customer satisfaction with support experiences.
• Generate content: RAG can help businesses produce blog posts, articles, product catalogs, or other content by combining its generative capabilities with retrieving information from reliable internal and external sources.
• Perform market research: By gathering insights from the vast volumes of data available on the internet—such as breaking news, industry research reports, even social media posts—RAG can keep businesses updated on market trends and analyze competitors’ activities, helping companies to make better decisions.
• Support sales: RAG can serve as a virtual sales assistant, answering customers’ questions about inventory, retrieving product specifications, explaining operating instructions, and assisting in the purchasing lifecycle. By marrying its generative abilities with product catalogs, pricing information, and other data, including customer reviews on social media, RAG can offer personalized recommendations, address customer concerns, and improve shopping experiences.
• Improve employee experience: RAG can help employees create and share a centralized repository of expert knowledge. By integrating with internal databases and documents, RAG can give employees accurate answers to questions about company operations, benefits, processes, culture, organizational structure, and more.

Challenges in RAG implementation

RAG can improve factual accuracy, but it does not remove the need for careful system design and oversight. A model can only work with the context it’s given. Weak retrieval, stale content, missing permissions, or poorly structured source material can result in incomplete or misleading answers.

• Retrieval quality: The model may generate a polished answer from irrelevant or incomplete context. Chunking strategy, metadata, reranking, and evaluation criteria all affect retrieval accuracy.
• Data freshness: A RAG application needs processes for updating indexes as policies, product information, and other external data change. Stale indexes produce stale answers.
• Latency: Query processing can include query embedding, vector search, filtering, reranking, prompt construction, and generation. Each step adds time.
• Access control: Retrieval must respect the source system’s permissions. Users should only receive answers based on documents they are authorized to access.
• Context limits: An LLM has a finite context window. Teams need to choose the most relevant context without overloading the prompt with loosely related material.
• Evaluation and monitoring: Organizations should test the full RAG process, including answer quality, citation accuracy, harmful outputs, and failures across common user queries.
• Complex relationships: Basic semantic search may struggle when a question depends on relationships across multiple records, versions, or systems. Knowledge graphs, structured retrieval, and parallel retrieval can all help in some cases.

Cohesity and AI

Cohesity Gaia brings conversational search and RAG to protected enterprise data. Users can ask questions in plain English, retrieve relevant content through semantic search and vector indexing, and receive cited answers grounded in source documents. Gaia applies permission-aware access controls to help keep enterprise information available only to authorized users.

Learn more about Cohesity Gaia and conversational search.

RAG FAQs

What is the full form of RAG in AI?

RAG in full form is “retrieval-augmented generation.” And what is retrieval-augmented generation? It describes an AI approach that retrieves relevant information from a knowledge source and adds it to an LLM’s prompt before the model generates a response. This can give the model access to current, private, or domain-specific information that was not part of its original training data.

Is RAG still relevant?

Yes, RAG is still relevant because enterprise data changes constantly, and organizations need AI systems that can access approved sources without retraining a model for every new or updated document. RAG is especially useful when answers need to be grounded in internal content, linked back to relevant documents, and governed by existing access permissions.

What is replacing RAG in AI?

RAG is not being replaced by any one successor technology. Long-context models, fine-tuning, agents, knowledge graphs, and tool-using AI systems can all complement a RAG architecture, depending on the use case. RAG remains a strong fit when an application must find relevant information across a large, changing knowledge base and use it to inform answers.