Retrieval-augmented generation (RAG) lets an application fetch relevant information from an external source, add it to a large language model’s prompt, and ask the model to answer using that context. It is a way to give a model access to specialized or changing information at answer time—not a guarantee that the information retrieved or the answer will be correct.
What is RAG in simple terms?
Think of RAG as an open-book answer process. The model receives a question, the application looks for useful passages in a collection of documents, and the model uses those passages as extra context when it responds. Without retrieval, the model is asked to rely on what it learned during training and whatever the user provides in the conversation.
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The name describes the sequence: retrieve information, augment the prompt with it, then generate a response. The foundational RAG paper describes the model’s learned knowledge as “parametric memory” and an external index as “non-parametric memory,” accessed by a retriever. In a user-facing application, the practical idea is simpler: look up relevant material before asking the model to answer. The original RAG paper introduced the approach for knowledge-intensive NLP tasks.
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How does an LLM answer questions from documents?
A typical system has a preparation phase and a question-answering phase. The preparation phase makes source material searchable; the answering phase finds likely relevant pieces and supplies them to the model.
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Prepare the knowledge source
- Collect the material. Connect or upload the documents the application should draw on, such as internal guides, product documentation, or reference material.
- Parse and split it. Extract usable text and divide it into smaller passages, often called chunks. Chunking gives retrieval a chance to return a focused excerpt rather than an entire document.
- Embed and index the passages. Embeddings represent text in a form that supports semantic matching. The system stores the passages and their representations in an index so they can be searched later. As one provider-specific example, OpenAI’s Retrieval documentation says files added to its vector stores are automatically chunked, embedded, and indexed: OpenAI Retrieval documentation.
Answer a user’s question
- Search for relevant passages. The application uses the question, and sometimes filters such as document type or access permissions, to retrieve candidate material.
- Add the passages to the prompt. The application provides the retrieved context alongside the original question and instructions for how to use it.
- Generate a response. The model produces an answer based on the prompt. A well-designed application can include references to the source passages or say when the available context is insufficient.
This is a common pattern, not a single required implementation. Retrieval systems may use semantic search, keyword search, a combination of methods, metadata filters, or other indexing approaches. LangChain’s retrieval overview describes retrieval as broader than any one vector-search setup.
Does RAG require a vector database?
No. A vector database is a common way to store and search embeddings, but it is not the definition of RAG. The essential idea is that an application retrieves external information and adds it to the model’s context before generation. Depending on the data and queries, a system might use semantic vector search, keyword search, hybrid retrieval, filters, or another kind of index.
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Semantic search can find passages that are conceptually related even when they do not share many exact words with the query. OpenAI’s documentation describes its Retrieval API as supporting semantic search that can surface semantically similar results “even when they match few or no keywords.” That capability is useful, but it does not mean every returned passage will be relevant enough to answer the question.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchHow is RAG different from fine-tuning?
RAG and fine-tuning solve different problems. RAG retrieves information and supplies it at inference time, when the application is answering a query. Fine-tuning changes a model’s behavior through additional training. RAG is often a fit when an application needs to consult a body of information; fine-tuning is aimed at changing how a model responds or performs a task. They can also be combined.
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Neither approach, by itself, proves that an application is accurate. OpenAI’s guidance on optimizing LLM accuracy treats retrieval as one dimension to optimize alongside other methods.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can go wrong, and how should RAG be evaluated?
RAG can help provide relevant, domain-specific context, but the final answer depends on the entire pipeline. If documents are incomplete or outdated, parsing loses important content, chunks split key context apart, or retrieval returns the wrong passages, the model may produce an unhelpful or unsupported answer. Even with good passages, the model may misunderstand or misuse them.
- Relevance: Does retrieval find the passages that actually answer representative questions?
- Coverage and freshness: Are the needed sources included, and how quickly do updates enter the index? Can obsolete records be removed?
- Retrieval design: Do semantic search, keyword search, hybrid methods, or metadata filters suit the questions and data?
- Permissions and operations: Does retrieval respect who may access each document, and can the index be maintained and monitored?
- Latency and cost: Account for query processing, retrieval, any reranking, generation, and index storage—not just the model call.
Test the complete system on representative questions, including cases where the answer is absent or the context is ambiguous. Check whether it retrieves the right evidence, whether the response is supported by that evidence, and whether it handles insufficient context appropriately. There is no single accuracy figure established for RAG that applies across tasks, and the approach does not guarantee a performance improvement in every application.
Costs are provider-specific. As listed in OpenAI’s Retrieval documentation accessed on 2026-10-07, storage beyond 1 GB is priced at $0.10 per GB per day. This is a changeable price for that provider’s service, not a general estimate of what a RAG system costs.
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