Back to Glossary 
6 min read Retrieval-augmented generation (RAG)
Retrieval-augmented generation, or RAG, is an AI method that retrieves relevant information from external sources before a large language model generates an answer. Its purpose is to ground the answer in selected, current, or private information that may not be available in the model’s training data.
A RAG system typically combines an information retrieval component with a generative model. The retriever finds useful passages, records, or other data, and the model uses that material as context when producing its response.
How retrieval-augmented generation works
A RAG pipeline usually has two parts: preparing information for retrieval and using that information to answer a request.
- Collect and prepare source data. The system connects to approved sources such as documents, websites, databases, product manuals, support articles, or company policies.
- Split and index the content. Long documents are commonly divided into smaller chunks. The system then creates an index that can be searched efficiently. Many RAG systems convert chunks into embeddings, which are numerical representations of their meaning.
- Process the user’s query. When someone asks a question, the retrieval system searches the index for relevant information. It may use keyword search, semantic search, database filters, or a combination of these methods.
- Retrieve the most relevant context. The system selects passages or records that appear most useful for answering the query. A reranking component may reorder the results before they reach the model.
- Augment the prompt. The retrieved context is added to the instructions and original query sent to the large language model.
- Generate the answer. The model produces a response using both its language capabilities and the supplied context. The application may also display citations that point to the retrieved sources.
The quality of the final answer depends on both stages. The system must retrieve the right information, and the model must use that information correctly.
Why RAG matters
A large language model learns patterns from its training data, but its internal knowledge is not a live database. It may lack recent information, private company data, or specialized knowledge. It may also produce a plausible answer even when it does not have enough reliable information.
Retrieval-augmented generation addresses this problem by giving the model relevant evidence at the time of the request. Organizations can update the connected knowledge sources without retraining the entire model.
RAG can also make answers easier to verify. When an application preserves source details, it can show users which documents or passages support a response. This does not guarantee accuracy, but it gives users a clearer basis for checking important claims.
Common requirements
A production RAG architecture normally requires:
- A defined set of trusted knowledge sources
- A process for cleaning, splitting, and indexing content
- A retrieval method suited to the data and query types
- Access controls for private or restricted information
- A large language model that can use retrieved context
- Monitoring and evaluation for retrieval and answer quality
- A method for updating or removing outdated information
Good source management is essential. Duplicate pages, unclear headings, inaccessible documents, missing metadata, and outdated content can all make retrieval less reliable.
Benefits of RAG
RAG gives AI applications controlled access to information beyond a model’s original training data. This supports several practical benefits:
- More current answers: The knowledge source can be updated without retraining the language model.
- Use of private information: Authorized users can ask questions about internal policies, records, or documentation.
- More relevant responses: Retrieval can focus the model on a particular organization, product, or technical domain.
- Source attribution: Applications can connect answers to supporting documents or passages.
- Greater control: Teams can decide which sources the system may retrieve and remove unreliable content.
- Lower adaptation cost: For many knowledge-based tasks, updating an index is more practical than training a model on every information change.
These benefits depend on the quality of the data, retrieval process, prompt, and model. Adding retrieval alone does not make a system reliable.
Limitations and risks
RAG does not eliminate hallucinations. A model can misunderstand retrieved material, combine sources incorrectly, or introduce unsupported claims. If retrieval returns irrelevant or incomplete context, the generated answer may still be wrong.
Other limitations include:
- Poor results from outdated, duplicated, or badly structured source content
- Loss of meaning when documents are divided into unsuitable chunks
- Retrieval failures caused by vague or complex queries
- Additional processing time and infrastructure costs
- Limited context windows that restrict how much evidence the model can use
- Difficulty evaluating answers without representative test questions
Security also requires careful design. A system must prevent users from retrieving documents they are not authorized to access. Teams should also account for sensitive-data exposure, malicious instructions inside retrieved content, unreliable external sources, and incorrect citations.
RAG accuracy should therefore be evaluated at two levels: whether the system retrieved the right evidence and whether the final answer is relevant, complete, and grounded in that evidence.
How retrieval-augmented generation is used
RAG is most useful when an AI application needs information that is specialized, frequently updated, too large to place in every prompt, or restricted to particular users.
Common uses include:
- A customer-support assistant retrieving product documentation and approved support articles
- An employee assistant answering questions from company policies and internal knowledge bases
- A research tool finding relevant passages across reports or academic papers
- A legal or compliance assistant retrieving authorized policies and regulations for human review
- An e-commerce assistant using current product, inventory, or delivery information
- An AI agent consulting brand guidelines, campaign data, or operating instructions before completing a task
For example, an AI agent supporting a social media strategy could retrieve approved brand terminology, audience research, and platform instructions before proposing content. Retrieval gives the agent relevant knowledge, while separate automation tools perform any approved action.
When AI agents need to work inside mobile apps, RAG and cloud phones serve different functions. RAG supplies relevant knowledge to the agent. An Android cloud phone provides the mobile environment in which an authorized workflow can operate.
Multilogin is a cloud phone platform for marketers, agencies, and AI workflow builders managing multiple social profiles. Its cloud phones, API, and ADB access may support the execution side of AI-agent workflows, but Multilogin is not a RAG platform.
Related technologies
Semantic search retrieves information based on meaning rather than relying only on exact keyword matches. It is frequently used in RAG pipelines.
Embeddings are numerical representations of content. They allow a retrieval system to compare the semantic similarity of queries and indexed information.
Vector databases store and search embeddings efficiently. They are common in RAG architectures, but they are not mandatory. A RAG system can use keyword search, SQL queries, search APIs, knowledge graphs, or hybrid retrieval instead.
Fine-tuning changes a model’s behavior or learned patterns by training it on additional examples. RAG supplies external context at query time. The two methods solve different problems and can be used together.
Agentic RAG allows an AI agent to plan or repeat retrieval steps, choose between data sources, reformulate queries, and use tools before producing an answer.
Key takeaways
Retrieval-augmented generation connects a language model to external knowledge before it answers a request. It is especially useful when an application needs current, private, specialized, or verifiable information without continually retraining the model.
A reliable RAG system needs more than a vector database. Its source quality, access controls, retrieval accuracy, prompt design, citations, and answer evaluation all affect the result.
People Also Ask
What is retrieval-augmented generation?
Retrieval-augmented generation is an AI method that retrieves relevant information from external sources and gives it to a large language model as context before the model generates an answer.
How does RAG work?
A RAG system searches an indexed knowledge source, selects relevant information, adds it to the model’s prompt, and asks the model to answer using that context.
What are the main components of a RAG system?
The main components are knowledge sources, a content-processing and indexing pipeline, a retriever, an optional reranker, a prompt-building layer, and a generative language model.
What is the difference between RAG and fine-tuning?
RAG provides external information when a query is processed. Fine-tuning changes a model by training it on additional examples. RAG is generally better suited to changing factual knowledge, while fine-tuning is often used to adjust behavior, format, or task performance.
Does RAG eliminate hallucinations?
No. RAG can reduce unsupported answers by supplying relevant evidence, but the model may still misinterpret that evidence or add claims that the retrieved sources do not support.
What are common RAG use cases?
Common uses include customer support, enterprise search, document Q&A, research assistance, product information, compliance support, and AI agents that require current or private knowledge.
Does RAG require a vector database?
No. Vector databases are common because they support semantic retrieval, but RAG can also use keyword search, relational databases, search APIs, knowledge graphs, or hybrid search.
What are the limitations and security risks of RAG?
RAG can retrieve incorrect, incomplete, outdated, or unauthorized information. Production systems need source governance, access controls, protection against malicious retrieved content, and separate evaluation of retrieval and generated answers.
