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Retrieval-augmented generation (RAG) lets an application fetch relevant information from an external source, add it to an AI model’s prompt, and ask the model to answer using that context. It is a way to give a model access to documents or other domain-specific information at answer time—not a guarantee that the answer will be correct.
What is RAG in simple terms?
Think of RAG as an assistant that looks up relevant passages before answering. Rather than relying only on information encoded in its learned parameters, the model receives selected material from an external knowledge source alongside the user’s question.
The name describes the sequence: the application retrieves information, augments the prompt with it, and asks the model to generate a response. The original RAG paper framed this as combining a model’s parametric memory with an external, non-parametric index. The foundational paper describes that research framing; modern applications commonly use the same broad retrieve-and-answer pattern.
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A typical RAG application prepares its knowledge source in advance, then searches it for useful passages whenever someone asks a question.
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- Collect the source material. Connect or upload the documents the application is allowed to use.
- Parse and split the material. Convert files into usable text and divide it into pieces small enough to find and send as context.
- Represent and index the pieces. A common approach creates an embedding for each piece and stores it in an index, often a vector store. OpenAI’s Retrieval documentation says files added to its vector stores are automatically chunked, embedded, and indexed.
- Retrieve passages for each question. The application searches for material likely to address the query. It may also apply filters, such as document type or access permissions.
- Augment the prompt. The application sends the question and selected passages to the model as context.
- Generate and present an answer. The model responds based on the prompt. A well-designed application can include source references or say when the available context is insufficient.
For example, an employee might ask, “How do I request parental leave?” A RAG system could search an HR policy collection, pass the relevant policy passages to the model, and ask it to explain the procedure. The answer depends on the policy being present, parsed correctly, retrieved, and used appropriately by the model.
Does RAG require a vector database?
No. A vector database or vector store is one common way to implement retrieval, not a defining requirement of RAG. Semantic search with embeddings can find text that is conceptually related to a query even when it does not share many exact terms. But applications can also use keyword search, metadata filters, hybrid search, or other retrieval methods. LangChain’s overview of retrieval discusses these different approaches.
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The best method depends on the information and the questions people ask. A system dealing with exact policy names, codes, or product identifiers may benefit from keyword matching; questions phrased differently from the source text may benefit from semantic retrieval. Combining methods or filtering results can help, but adds design and evaluation work.
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No. RAG supplies external information in the prompt at inference time. Fine-tuning changes a model’s behavior through training. They address different needs, and an application can use them together.
| Approach | What changes | Often useful for |
|---|---|---|
| RAG | Information retrieved and added to the prompt for a particular request | Providing context from documents or information that may change |
| Fine-tuning | The model’s behavior through additional training | Adapting how a model responds or performs a task |
Neither approach, by itself, establishes that an application will be accurate. OpenAI’s accuracy guidance treats retrieval as one dimension to optimize alongside other methods.
Does RAG prevent hallucinations?
No. RAG can give a model useful evidence, but it does not eliminate unsupported or incorrect answers. The system may retrieve irrelevant, incomplete, or outdated passages, or the model may misinterpret the context. The result also depends on the source material, parsing and chunking, retrieval, prompt construction, and how the model uses what it receives.
There is no single accuracy figure that applies to all RAG systems or a guaranteed improvement across tasks. Evaluate the complete application with representative questions, including cases where the answer is absent or ambiguous. Check whether it retrieves the right passages, handles insufficient evidence appropriately, and reflects source updates as intended.
What should you consider when designing a RAG system?
- Relevance: Does retrieval surface passages that actually answer the question?
- Retrieval method: Do semantic, keyword, hybrid, or filtered searches fit the data and query patterns?
- Freshness: How quickly do updates reach the index, and how are obsolete records removed?
- Latency and cost: Account for query processing, retrieval, any reranking, model generation, and index storage. As one provider-specific example, OpenAI’s Retrieval documentation listed storage beyond 1 GB at $0.10/GB/day when accessed on October 7, 2026; this is a changeable service price, not a general estimate of RAG costs.
- Operational work: Consider ingestion, access permissions, evaluation, monitoring, and maintaining a separate index.
RAG is most useful when an application needs to bring relevant external information into a model’s answer process. Its value depends on the quality and maintenance of the whole retrieval-and-generation pipeline, not simply on adding a vector store.
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