Retrieval-Augmented Generation (RAG)

Large language models know a great deal, but what they know comes from their training data, which has a cutoff date and does not include an organization's private documents, policies, records or recent events. Ask a model about your company's travel policy or last quarter's results, and it will either refuse or, worse, invent a plausible answer. Retrieval-augmented generation solves this by finding the relevant information first and giving it to the model along with the question. The model then answers based on that information, and the system can show which sources it used.

The term comes from a 2020 research paper by Patrick Lewis and colleagues at Facebook AI Research, now Meta, which combined a retrieval system with a generative model to improve performance on knowledge-intensive tasks. RAG has since become one of the most widely used patterns for enterprise AI, powering internal knowledge assistants, customer support bots, document search and research tools. Its basic form uses semantic search over chunks of documents, often stored in a vector database, but approaches have grown more sophisticated, combining keyword and semantic search, structured data and knowledge graphs. For AI engineers, knowledge managers, IT leaders and anyone deploying AI on company information, RAG is a core technique.

Definition of Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation is an AI architecture in which, for each request, a retrieval component searches one or more knowledge sources for relevant content, and that content is added to the prompt of a generative model so that the model's response is grounded in current, specific and verifiable information rather than relying only on its training data.

It is a common method of AI grounding, often uses a vector database and can be extended with knowledge graphs in GraphRAG.

How RAG Works

Ingestion. Documents and data are collected, split into chunks and indexed, often by converting them into numerical representations called embeddings.

Retrieval. When a user asks a question, the system searches the index for the most relevant chunks, using semantic search, keyword search or both.

Augmentation. The retrieved content is added to the prompt, along with instructions on how to use it.

Generation. The model writes an answer based on the provided content, often with citations to its sources.

Evaluation. Answers are checked for accuracy and relevance, and the system is tuned based on the results.

Common RAG Variations

Hybrid search. Combining semantic and keyword search to improve retrieval accuracy.

Reranking. Using a second model to reorder retrieved results by relevance before passing them to the generator.

Agentic RAG. An agent decides what to search for, runs several searches and refines its query until it has enough information.

Structured retrieval. Querying databases or knowledge graphs alongside documents for precise facts and relationships.

Importance of RAG in Enterprise Operations

It makes AI useful on company information. Assistants can answer questions about policies, products, customers and processes accurately. Employees get answers in seconds rather than searching through intranets and shared drives.

It reduces invented answers. Grounding responses in retrieved sources lowers the risk of fabricated information. Instructions can also tell the model to say when the sources do not contain an answer.

It also supports trust and compliance. Citations let users check answers, and access controls can ensure people see only what they are permitted to see.

RAG and Financial Management

RAG is usually far cheaper than training or fine-tuning models on company data, and it keeps information current without retraining.

Costs come from indexing, storage, retrieval and the extra tokens added to each prompt, which grow with usage. Long prompts filled with retrieved text are often the largest driver of running costs.

Retrieving only the most relevant content keeps prompts short and reduces model costs.

Value can be measured through reduced search time, faster support resolution and fewer escalations. These benefits are easiest to show when baseline measures are recorded before launch.

Common Challenges

Poor retrieval returns irrelevant or incomplete content, leading to weak answers even with a strong model. If the right passage is not retrieved, the model cannot use it.

Source content is outdated, duplicated or contradictory, and the AI repeats the problems. A RAG system is only as reliable as the content it draws on, so knowledge management matters as much as the technology.

Access controls are not applied at retrieval time, risking exposure of sensitive information. A user should never receive an answer based on a document they could not open themselves.

Systems are not evaluated systematically, so quality problems go unnoticed. Without measurement, teams cannot tell whether changes to the system make it better or worse.